Another confusing conflict in ElectricAccelerator

After solving the case of the confounding conflict, my user came back with another scenario where ElectricAccelerator produced an unexpected (to him) conflict:

@$(MAKE) foo
@cp foo bar
@sleep 2 && echo hello world > foo

If you run this build without a history file, using at least two agents, you will see a conflict on the continuation job that executes the cp foo bar command, because that job is allowed to run before the job that creates foo in the recursive make invocation. After one run of course, emake records the dependency in history, so later builds don’t make the same mistake.

This situation is a bit different from the symlink conflict I showed you previously. In that case, it was not obvious what caused the usage that triggered the conflict (the GNU make stat cache). In this case, it’s readily apparent: the continuation job reads (or attempts to read) foo before foo has been created. That’s pretty much a text-book example of the sort of thing that causes conflicts.

What’s surprising in this example is that the continuation job is not automatically serialized with the recursive make that precedes it. In a very real sense, a continuation job is an artificial construct that we created for bookkeeping reasons internal to the implementation of emake. Logically we know that the commands in the continuation job should follow the commands in the recursive make. In fact it would be absolutely trivial for emake to just go ahead and stick in a dependency to ensure that the continuation is not allowed to start until after the recursive make finishes, thereby avoiding this conflict even when you have no history file.

Given a choice between two strategies that both produce correct output, emake uses the strategy that produces the best performance in the general case.

Absolutely trivial to do, yes — but also absolutely wrong. Not for correctness reasons, this time, but for performance. Remember, emake is all about maximizing performance across a broad range of builds. Given a choice between two strategies that both produce correct output, emake uses the strategy that produces the best performance in the general case. For continuation jobs, that means not automatically serializing the continuation against the preceding recursive make. I could give you a wordy, theoretical explanation, but it’s easier to just show you. Suppose that your makefile looked like this instead of the original — the difference here is that the continuation job itself launches another recursive make, rather than just doing a simple cp:

@$(MAKE) foo
@$(MAKE) bar
@sleep 2 && echo hello world > foo
@sleep 2 && echo goodbye > bar

Hopefully you agree that the ideal execution of this build would have both foo and bar running in parallel. Forcing the continuation job to be serialized with the preceding recursive make would choke the performance of this build. And just in case you’re thinking that emake could be really clever by looking at the commands to be executed in the continuation job, and only serializing “when it needs to”: it can’t. First, that would require emake to implement an entire shell syntax parser (or several, really, since you can override SHELL in your makefile). Second, even if emake had that ability, it would be thwarted the instant the command is something like — there’s no way to tell what will happen when that gets invoked. It could be a simple filesystem access. It could be a recursive make. It could be a whole series of recursive makes. Even when the command is something you think you recognize, can emake really be sure? Maybe cp is not our trustworthy standard Unix cp, but something else entirely.

Again, all is not lost for this user. If you want to avoid this conflict, you have a couple options:

  1. Use a good history file from a previous build. This is the simplest solution. You’ll only get conflicts in this build if you run without a history file.
  2. Refactor the makefile. You can explicitly describe the dependency between the commands in the continuation job and the recursive make by refactoring the makefile so that the stuff in the continuation is instead its own target, thus taking the decision out of emake’s hands. Here’s one way to do that:
    all: do_foo
    @cp foo bar
    @$(MAKE) foo
    @sleep 2 && echo hello world > foo

Either of these will eliminate the conflict from your build.

ElectricAccelerator and the Case of the Confounding Conflict

A user recently asked me why ElectricAccelerator reports a conflict in this simple build, when executed without a history file from a previous run:

all: foo symlink_to_foo
@sleep 2 && echo hello world > foo
@ln -s foo symlink_to_foo

Specifically, if you have at least two agents, emake will report a conflict between symlink_to_foo and foo, indicating that symlink_to_foo somehow read or otherwise accessed foo during execution! But ln does not access the target of a symlink when creating the symlink — in fact, you can even create a symlink to a non-existent file if you like. It seems obvious that there should be no conflict. What’s going on?

To understand why this conflict occurs, you have to wrap your head around two things. First, there’s more going on during a gmake-driven build than just the commands you see gmake invoke. That causes the usage that provokes the conflict. Second, emake considers a serial gmake build the “gold standard” — if a serial gmake build produces a particular result, so too must emake. That’s why the additional usage must result in a conflict.

In this case, the usage that triggers the conflict comes from management of the gmake stat cache. This is a gmake feature that was added to improve performance by avoiding redundant calls to stat() — once you’ve stat()‘d a file once, you don’t need to do it again. Unless the file is changed of course, which happens quite a lot during a build. To keep the stat cache up-to-date as the build progresses, gmake re-stat()‘s each target after it finishes running the commands for the target. So after the commands for symlink_to_foo complete, gmake stat()‘s symlink_to_foo again, using the standard stat() system call, which follows the symlink (in contrast to lstat(), which does not follow the symlink). That means gmake will actually cache the attributes of foo for symlink_to_foo.

To ensure compatibility with gmake, emake has to do the same. In Accelerator parlance, that means we get read usage on symlink_to_foo (because you have to read the symlink itself to determine the target of the symlink), and lookup usage on foo. The lookup on foo causes the conflict, because, of course, you will get a different result if you lookup foo before the job that creates it than you would get if you do the lookup after that job. Before the job, you’ll find that foo does not exist, obviously; after, you’ll find that it does.

But what difference does that make, really? In truth, if there’s no detectable difference in behavior, then it doesn’t matter at all. And in the example build there is no detectable difference — the build output is the same regardless of when exactly you stat() symlink_to_foo relative to when foo is created. But with a small modification to the build, it is suddenly becomes possible to see the impact:

all: foo symlink_to_foo reader
@sleep 2 && echo hello world > foo
@ln -s foo symlink_to_foo
reader: foo symlink_to_foo
@echo newer prereqs are: $?

Compare the output when this build is run serially with the output when the build is run in parallel — and note that I’m using gmake, so you can be certain I’m not trying to trick you with some peculiarity of emake’s implementation:

You can plainly see the difference: in the parallel build gmake stat()‘s symlink_to_foo before foo exists, so the stat cache records symlink_to_foo as non-existent. Then when gmake generates the value of $? for reader, symlink_to_foo is excluded, because non-existent files are never considered newer than existing files. In the serial build, gmake stat()‘s symlink_to_foo after foo has been created, so the stat cache indicates that symlink_to_foo exists and is newer than reader, so it is included in $?.

Hopefully you see now both what causes the conflict, and why it is necessary. The conflict occurs because of lookup usage generated when updating the stat cache. The conflict is necessary to ensure that the build output matches that produced by a serial gmake — the “gold standard” for build correctness. If no conflict is declared, there is the possibility for a detectable difference in build output compared to serial gmake.

However, you might be thinking that although it makes sense to treat this as a conflict in the general case, isn’t it possible to do something smarter in this specific case? After all, the orignal example build does not use $?, and without that there isn’t any detectable difference in the build output. So why not skip the conflict?

The answer is simple, if a bit disappointing. In theory it may be possible to elide the conflict by checking to see if the symlink is used by a later job in a manner that would produce a detectable difference (for example, by scanning the commands for subsequent targets for references to $?), but in reality the logistics of that check are daunting, and I’m not confident that we could guarantee correct behavior in all cases.

Fortunately all is not lost. If you wish to avoid this conflict, you have several options:

  1. Use a good history file from a previous build. This is the most obvious solution. You’ll only get conflicts if you run without a history file.
  2. Add an explicit dependency. If you make foo an explicit prereq of symlink_to_foo, then you will avoid the conflict. Here’s how that would look:
    symlink_to_foo: foo
  3. Change the serial order. If you reorder the makefile so that symlink_to_foo has an earlier serial order than foo you will avoid the conflict. That just requires a reordering of the prereqs of all:
    all: symlink_to_foo foo

Any one of these will eliminate the conflict from your build, and you’ll enjoy fast and correct parallel builds.

Case closed.

Makefile hacks: automatically split long command lines

If you’ve worked on a large build system you’ve probably bumped into this error, or one like this:

gmake: execvp: /bin/sh: Argument list too long

This error means the length of some command-line in your makefile has grown past the system limit, which is typically in the 32 to 256 kilobyte range. It’s surprisingly easy to hit that limit. You start with a small list of object files to be linked together. Over time you add more, and the command-line gets a little longer. Add a few more and it gets longer still. Before you know it you have a monster command-line and your build starts failing.

The solution to this problem is simple: split the long command-line into several shorter command-lines. For example, ar r libraries/lib.a objects/foo.o objects/bar.o objects/baz.o objects/boo.o objects/bang.o becomes something like this:

ar r libraries/lib.a objects/foo.o objects/bar.o
ar r libraries/lib.a objects/baz.o objects/boo.o
ar r libraries/lib.a objects/bang.o

Simple in theory, but tedious to do by hand. And doing it manually is like putting a ticking time-bomb into your makefile — it’s only a matter of time before your build grows enough that you have to go through this exercise again.

I recently ran across a clever solution that exploits the $(eval) function in GNU make to split long command-lines automatically, eliminating the tedium and the time-bomb. After I show you the solution, I’ll explain it piece-by-piece.

The max_args function

The solution is a user-defined function called max_args that splits long command-lines into equal-length chunks:

define max_args
$(eval _args:=)
$(foreach obj,$3,$(eval _args+=$(obj))$(if $(word $2,$(_args)),$1$(_args)$(EOL)$(eval _args:=)))
$(if $(_args),$1$(_args))
define EOL

And an example of its use:

OBJS:=a b c d e f g h
@$(call max_args,echo,2,$(OBJS))

The max_args function takes three parameters: the base command-line, the number of arguments per “chunk”, and the complete list of arguments. It expands to a series of command-lines — one for each chunk of arguments.

The trick behind max_args is the use of $(eval) to update a variable as a side-effect of gmake’s regular variable expansion activity. If you’re not familiar with gmake variable expansion, here’s a quick rundown: when gmake finds a variable or function reference, like $(something), it replace the entire reference with an expanded value. In the case of a variable that’s just the value of the variable. Most variables in gmake are recursive which means that if the variable value itself contains embedded variable references, those will be expanded as well, recursively. In the case of a function, gmake evaluates the function, and replaces the reference with the computed value.

The meat of max_args is on line 3. It starts with the $(foreach) function, which evaluates its third argument, the body of the loop, once for each word in its second argument — in this case, the list of objects passed in the call to max_args.

In max_args, the loop body has two components. The first is a call to $(eval), which simply appends the current value of the loop variable to an accumulator called _args.

The second component of the loop body uses $(if) and $(word) to check the length of _args. The $(word) function returns the nth word from a list, or an empty string if there are fewer than n words in the list. The $(if) function expands its second argument (the then clause) only if its first argument (the condition) expands to a non-empty string, so together these functions check if _args has the desired number of words, and if so the then clause of the $(if) is expanded.

The then clause of this $(if) has two components. The first constructs a completed command-line by concatenating the base command-line, here given by $1, the first argument to the original max_args call; the accumulated arguments; and a newline character. Thanks to the rules of gmake expansion, this command-line is added to the overall expansion result for the max_args function. The second part of the then clause uses $(eval) to reset the accumulator

If the chunk size does not evenly divide the number of arguments, the stragglers are emitted in a final command-line on the last line of max_args.


max_args is handy but it has one significant limitation: command-line length limits are based on the number of bytes in the command-line, not the number of words, in it. Unfortunately, gmake has no built-in way to count the number of characters in a string. gmake does provide the $(words) built-in, so that’s what max_args uses. That just means that to use it effectively you have to take a guess at the number of arguments that will fit in a single command-line, for example by dividing the length limit by the average number of characters in each argument, then subtracting some to allow some buffer for outliers.

Exceptions to conflict detection in ElectricMake

In a previous article I covered the basic conflict detection algorithm in ElectricMake. It’s surprisingly simple, which is one of its strengths. But if ElectricMake strictly adhered to the simple definition of a conflict, many builds would be needlessly serialized, sapping performance. Over the years we’ve made a variety of tweaks to the core algorithm, adding support for special cases to improve performance. Here are some of those special cases.

Non-existence conflicts

One obvious enhancement is to ignore conflicts when the two versions are technically different, but effectively the same. The simplest example is when there are two versions of a file which both indicate non-existence, such as the initial version and the version created by job C in this chain for file foo:

Suppose that job D, which falls between C and E in serial order, runs before any other jobs finish. At runtime, D sees the initial version, but strictly speaking, if it had run in serial order it would have seen the version created by job C. But the two versions are functionally identical — both indicate that the file does not exist. From the perspective of the commands run in job D, there is no detectable difference in behavior regardless of which of these two versions was used. Therefore emake can safely ignore this conflict.

Directory creation conflicts

A common make idiom is mkdir -p $(dir $@) — that is, create the directory that will contain the output file, if it doesn’t already exist. This idiom is often used like so:

$(OUTDIR)/foo.o: foo.cpp
	@mkdir -p $(dir $@)
	@g++ -o $@ $^

Suppose that the directory does not exist when the build starts, and several jobs that employ this idiom start at the same time. At runtime they will each see the same filesystem state — namely, that the output directory does not exist. Each job will therefore create the directory. But in reality, had these jobs run serially, only the first job would have created the directory; the others would have seen the version created by the first job, and done nothing with the directory themselves. According to the simple definition of a conflict, all but the first (serial order) job would be considered in conflict. For builds without a history file expressing the dependency between the later jobs and the first, the performance impact would be disastrous.

Prior to Accelerator 5.4, there were two options for avoiding this performance hit: use a good history file, or arrange for the directories to be created before the build runs. Accelerator 5.4 introduced a refinement to the conflict detection algorithm which enables emake to suppress the conflict between jobs that both attempt to create the same directory, so even builds with no history file will not get conflicts in this scenario, without sacrificing correctness. (NB: you need not take special action to enjoy the benefits of this improvement).

Appending to files

Another surprisingly common idiom is to append error messages to a log file as the build proceeds:

$(OUTDIR)/foo.o: foo.cpp
	@g++ -o $@ $^ 2>> err.log

Implicitly, each append operation is dependent on the previous appends to the file — after all, how will you know which offset the new content should be written to if you don’t know how big the file was to begin with? In terms of file versions, you can imagine a naive implementation treating each append to the file as creating a complete new version of the file:

The problem of course is that you’ll get conflicts if you try to run all of these jobs in parallel. Suppose all three jobs, A, B and C start at the same time. They will each see the initial version, an empty file, but if run serially, only A would have seen that version. B would have seen the version created by A; C would have seen the version created by B.

This example is particularly interesting because emake cannot sort this out on its own: as long as the usage reported for err.log is the very generic “this file was modified, here’s the new content” message normally used for changes to the content of an existing file, emake has no choice but to declare conflicts and serialize these jobs. Fortunately, emake is not limited to that simple usage record. The EFS can detect that each modification is strictly appending to the file, with no regard to the prior contents, and include that detail in the usage report. Thus informed, emake can record fragments of the file, rather than the entire file content:

Since emake now knows that the jobs are not dependent on the prior content of the file, it need not declare conflicts between the jobs, even if they run in parallel. As emake commits the modifications from each job, it stitches the fragments together into a single file, with each fragment in the correct order relative to the other pieces.

Directory read conflicts

Directory read operations are interesting from the perspective of conflict detection. Consider: what does it mean to read a directory? The directory has no content of its own, not in the way that a file does. Instead, the “content” of a directory is the list of files in that directory. To check for conflicts on a directory read, emake must check whether the list of files that the reader job actually saw matches the list that it would have seen had it run in serial order — in essence, doing a simple conflict check on each of the files in the directory.

That’s conceptually easy to do, but the implications of doing so are significant: it means that emake will declare a conflict on the directory read anytime any other job creates or deletes any file in that directory. Compare that to reads on ordinary files: you only get a conflict if the read happens before a write operation on the same file. With directories, you can get a conflict for modifications to other files entirely.

This is particularly dangerous because many tools actually perform directory reads under-the-covers, and often those tools are not actually concerned with the complete directory contents. For example, a job that enumerates files matching *.obj in a directory is only interested in files ending with .obj. The creation of a file named foo.a in that directory should not affect the job at all.

Another nasty example comes from utilities that implement their own version of the getcwd() system call. If you’re going to roll your own version, the algorithm looks something like this:

  1. Let cwd = “”
  2. Let current = “.”
  3. Let parent = “./..”
  4. stat current to get its inode number.
  5. read parent until an entry matching that inode number is found.
  6. add the name from that entry to cwd
  7. Set current = parent.
  8. Set parent = parent + “/..”
  9. Repeat starting with step 4.

By following this algorithm the program can construct an absolute path for the current working directory. The problem is that the program has a read operation on every directory between the current directory and the root of the filesystem. If emake strictly adhered to conflict checking on directory reads, a job that used such a tool would be serialized against every job that created or deleted any file in any of those directories.

For this reason, emake deliberately ignores conflicts on directory read operations by default. Most of the time this is safe to do, surprisingly — often tools do not need a completely accurate list of the files in the directory. And in every case I’ve seen, even if the tool does require a perfectly correct list, the tool follows the directory read with reads of the files it finds. That means that you can ensure correct behavior by running the build one time with a single agent, to ensure the directory contents are correct when the job runs. That run will produce history based on the file reads, so subsequent builds can run with many agents and still produce correct results.

Starting with Accelerator 6.0, you can also use –emake-readdir-conflicts=1 to force emake to honor directory read conflicts.


Getting parallel builds that are fast is easy: just add -j to your make invocation. Getting parallel builds that are both fast and reliable is another story altogether. As you’ve seen, the core conflict detection algorithm in ElectricMake is simple, but after many years and hundreds of thousands of builds, we’ve enhanced that simple algorithm in a variety of special cases to provide even better performance. Future releases of ElectricAccelerator will include even more refinements to the algorithm.

HOWTO: diagnose build failures with ElectricMake and ElectricInsight

The other day a colleague asked for my help determining the cause of a broken build. When run with ElectricMake, the build consistently failed with this error:

Error: Could not open include file "api.h".
make: *** [js/src/jsreflect.o] Error 1

Diagnosing problems like this is similar to investigation in any scientific field: form a hypothesis, devise an experiment to test it, and use the results of the experiment to refine the hypothesis; then repeat until you can explain the observed behavior. In this case I didn’t have access to the build environment so I couldn’t run builds myself, which limited my ability to experiment somewhat. Instead, I had to rely on the data my colleague provided: an emake annofile from the build, with lookup-level logging. If you can only get one build artifact for debugging, that’s a pretty good choice.

Hypothesis: api.h does not exist

The first thing to check is whether the file api.h exists at all. If it doesn’t, that would explain the failure. Of course, I had been told that this build works with gmake, so this is a pretty flimsy hypothesis — but it pays to be thorough.

One way to test this theory is to check the usage reported on the file. If the only usage is failed lookups, then the file never existed. If you see other usage, like reads or modifications to the file, that invalidates this hypothesis. We can use grep to search the annotation for usage on the file:

The file is clearly created during the build, so this theory is BUSTED.

Hypothesis: the writer job comes after the reader job

Since the file is created during the build, perhaps the problem is that the job that created it occurs later in the build than the job that needs it. That could happen if the makefile was set up incorrectly, for example. Like the previous theory, this one is on shaky ground because the build allegedly works with gmake. It’s easy to test though: find the job that created the file, and the job that reported the error, then compare their serial order — the order in which they would have run if the build were executed serially. If the writer has a later serial order, then the hypothesis is confirmed. Otherwise, the hypothesis is invalidated.

To find the writer job, I use less to search for the create operation referencing that file:

Then I search backwards for the containing <job> tag to determine which job created the file:

Now we know the file was created by job J003286c8. The easiest way to find the job’s serial order is to load the annotation in ElectricInsight, then bring up the Job Details window for the job (use Tools -> Find job by ID and enter the job to go directly to the Job Details):

To find the job that reported the error, search for failed jobs in ElectricInsight. That leads us to job J0032a168:

The writer has an lower serial order than the reader, meaning the writer comes first. Therefore this theory is also BUSTED.

Hypothesis: the reader job ran before the writer job finished

Since the writer precedes the reader in serial order, perhaps the problem is that the jobs were executed in the wrong order. We can test this hypothesis by checking the start and end time of each job, again by looking at the Job Details in ElectricInsight. Here’s the writer:

And the reader:

The jobs were actually running at the same time so this hypothesis is CONFIRMED.

Now we know why the reader failed to find the file: it ran before the writer finished, so naturally the file was not available. But this raises an entirely new and more perplexing question: this is precisely the type of dependency problem that ElectricMake is supposed to prevent, so why didn’t it work this time?

Hypothesis: conflict detection is completely broken

If everything is working correctly, emake ought to have detected a conflict in the reader job, discarded the bogus result, and rerun the job after the writer finished. A bug in emake’s conflict detection system could explain why emake failed to detect the dependency between the jobs and rerun the reader. We could construct any number of elaborate tests to try to prove that conflict detection is broken, but before we disappear down that rabbit hole, we should check the file usage recorded in the reader job. If we find usage that should have caused a conflict with the writer job, then we can continue with this line of investigation. But if there is no such usage, then we can reject this theory.

For an overview of conflict detection, read “How ElectricMake guarantees reliable parallel builds”. Briefly, in order for conflict detection to work there must be usage in the reader that references api.h. That’s how emake knows that the reader tried to use the file. When emake checks for conflicts, it will see that usage, and realize that the job accessed the wrong version of the file based on the serial order of the job. At minimum we should see a lookup operation recorded on the file.

We can find the file usage for the reader on the Annotation tab of the Job Details for the job in ElectricInsight. You can use CTRL-F to search for occurances of the string api.h in the job annotation. But in this case, there’s only one, in the error message text. There’s no usage recorded for the file, lookup or otherwise:

So this theory is clearly BUSTED. There was no conflict because there was no usage on the file in the reader job. But again, this result raises a new question: why is there no usage referencing api.h?

Hypothesis: there was a problem accessing the parent directory

A problem accessing the parent directory would explain why there is no usage for api.h. After all, you can’t lookup a file in a directory if you can’t access the directory itself. To verify this theory we have to check for usage on the parent directory, of course. If there is none, then we can consider the theory confirmed, and we will have to come up with an explanation for that failure. But if there is usage on the parent directory, we can reject this theory. Specifically, we ought to see a lookup recorded for the parent directory, captured as a side effect of the compiler accessing files in the directory.

So we turn again to the Annotation tab of the Job Details for the reader job in ElectricInsight. This time we’ll search for the string build/view/src, which turns up just one match:

There is usage recorded for the parent directory, so this theory is BUSTED.

But there’s a surprise lurking in the result: instead of a lookup, we see a read recorded on the directory. Why is that surprising? Consider what a compiler does: read the source file, locate and read include files, and write the output file. Nothing in that description requires reading directories. This leads us to a new hypothesis, which explains both the peculiar usage and the build failure.

Hypothesis: the compiler caches directory listings to avoid system calls

The simplest way to search for include files is to stat() the file in each directory given in the include path. If the stat() succeeds, you’ve found the file; if not, try the next directory.

This is simple, but inefficient if you have many directories in your include path. Suppose you have 300 directories, and 10 include files. On average you’ll check half of the directories before finding each file, for a total of 1,500 stat() calls! As everybody “knows”, system calls are slow, so some clever compilers use a different strategy: cache the listing of each directory in a hash table, then consult this cache, rather than using stat(). With 300 directories, you can do a few hundred getdents() system calls, instead of thousands of stat() calls. Brilliant!

There’s just one problem: this trick conceals from emake the fact that the job tried to find api.h. Since the lookup never hit the filesystem, emake has no record of it, and therefore cannot detect the conflict.

Sidebar: directory read conflicts

Of course, emake can still detect the conflict — by comparing the contents of the directory as they were with the contents as they should have been. That is, emake can tell that the reader job got a particular set of filenames for the directory listing, and that the set would have been different if the reader had run at the correct time — it would have included api.h.

This is an example of a directory read conflict. The important thing to know is that emake deliberately ignores these conflicts. If it didn’t, many builds would be horribly over-serialized — usually when programs read directories during a build, they don’t actually care about the entire directory listing. If emake strictly honored directory read conflicts, a job that read a directory would be serialized against every job that created or deleted any file in that directory. Nobody wants that.

Fortunately, there’s a solution: once the compiler has found the include file, it goes on to read it, of course. The read gets recorded in the job’s file usage, and that gives emake enough information to properly serialize the reader and the writer. So we need only to ensure that the filesystem state is correct when the reader runs, for a single run of the build. After that, emake will record the dependency in its history file, which will ensure correct behavior in subsequent builds. One easy way to do this is to run a single-agent build, using –emake-maxagents=1. That forces each job to run serially. This mode is how we will test our final hypothesis. If we’re correct, then the build will succeed; if not, the build will still fail.


As I stated, I didn’t have access to this build myself, so I had to wait for the user to test this hypothesis. When they did, they found that the single-agent build succeeded, and by checking the file usage for the reader job in the new build, we can see a read of api.h, as expected. Our final theory is CONFIRMED: the build failed because the compiler caches directory listings instead of doing direct filesystem lookups for include files, and emake intentionally ignores directory read conflicts.

The simplest solution to the problem is to generate a good history file by running emake with –emake-maxagents=1, but you could also add an explicit dependency between the jobs in the makefile; or you could wait for ElectricAccelerator 6.0, which will include a feature that allows you to explicitly enable directory read conflicts with a command-line option.

ElectricAccelerator Job Compendium

The fundamental unit of work in ElectricAccelerator is the job. Most of the time, you can think of a job as all the commands that must be run in order to create or update a single build output, but in truth that describes only one type of job. There are actually several different job types, each with a distinct purpose in the structure of a build and the way Accelerator executes the build. You can determine the type of a job from the type attribute on the <job> tag in Accelerator annotation files.

Having some familiarity with the job types and their use will make it easier for you to understand Accelerator performance and behavior, so I wrote this guide to introduce them. First I’ll describe the jobs used by ElectricMake (emake), and then the jobs used by Electrify.

ElectricMake jobs

In order to make the descriptions more concrete, I created a simple reference build that uses each of the most common job types, so you can see exactly how the jobs relate to a real build. Here’s the reference build makefile:

        @$(MAKE) sub/prog
        @cp sub/prog ./prog

sub/prog: sub/main.c
        @cat $< > $@

        rm -rf prog sub
        mkdir sub
        echo "int main() { return 0; }" > sub/main.c
        touch prog2

To run the build, first run emake setup, which will create the files and directory structure needed by the build, then run emake –emake-maxagents=1 –emake-annodetail=basic –emake-annofile=emake.xml prog prog2. That will produce a build with thirteen different jobs, in two different make instances. Here’s an overview of how those jobs fit together (click for a larger image):


The first job in any make invocation (and therefore the first job of any emake build) is a parse job, during which emake reads and interprets the makefiles used in that make instance. The output of a parse job is a list of all the jobs in the make instance, along with a list of targets that must be built, the commands to build those targets, and the relationships between them.


Existence jobs (marked as type “exist” in annotation) are used to check for the existence of makefiles and command-line goals for which no rule was found. In our reference build, you can see an existence job in the top-level make for the makefile itself, as well as one for the file prog2, which has no rule in the makefile.


Remake jobs are at the center of emake’s emulation of GNU make makefile remaking feature. In a remake job, emake checks every makefile that was read during the parse job for two things:

  1. Is there a rule to rebuild the makefile; and
  2. Was the makefile actually rebuilt (because it was out-of-date).

If any makefile was rebuilt, then emake restarts the make instance — all the way back to the parse job.

Because makefile remaking is a gmake-specific feature, you will only see remake jobs when emake is emulating gmake — not when it’s emulating NMAKE.

One last note about remake jobs: emake overloads the use of the <failed> tag inside a remake job to indicate not failure, but whether or not the job determined that the make instance should be restarted. If yes, then the remake job will include a <failed> tag with the code attribute set to 1; if not, then the remake job will have no <failed> tag.


Rule jobs are the real workhorses of a build. Each rule job encapsulates the commands needed to update one target in the build — literally the body of a rule from a makefile — and a rule always has an associated output target (or targets), which is identified by the name attribute of the job tag in annotation. In our reference build, the rule job in the toplevel make instance corresponds to this rule in the makefile:

        @$(MAKE) sub/prog
        @cp sub/prog ./prog

If you look at the annotation for the job you’ll notice that only $(MAKE) sub/prog is actually in the rule job — because the cp sub/prog ./prog was automatically split into a continuation job when emake detected the recursive make invocation.


A follow job serves two purposes. First, it is a connection point that allows emake to tie the end job of a recursive make invocation back into the ordered list of jobs in the parent make. Second, it is the means by which emake propagates the error status of a recursive make to the parent make.

Follow jobs always have an associated rule or continuation job, identified by the partof attribute on the job tag in annotation. That is the job that spawned the recursive make which the follow job is associated with. Of course, not all rule and continuation jobs have an associated follow job — follow jobs only show up when a recursive make was invoked.


A continuation job represents the “leftover” commands that follow a recursive make invocation in a rule (or continuation) job. In our reference build, emake creates a continuation job for cp sub/prog ./prog, because that command follows a recursive make invocation. Continuation jobs, like follow jobs, are always associated with a rule (or another continuation) job, identified by the partof attribute on the job tag in the annotation.

The reason for splitting the extra commands into a separate job is simple: that allows emake to easily specify the serial order of those commands relative to the jobs in the recursive make — the commands in the continuation come after the jobs in the recursive make.


The last job in every make instance (and therefore the last job of every emake build) is an end job. End jobs exist primarily to handle end-of-the-make cleanup, such as removing intermediate targets or temporary inline files that were created while executing the other jobs in the make.

(Not pictured) subbuild

Subbuild jobs, which were not used in the reference build, are part of emake’s subbuild feature. They are simply a mechanism to inject a recursive make invocation into the build, just as you would get if you had a rule job with a $(MAKE) command, but without the tedium of actually running that command.

Electrify jobs

Electrify uses much of the same underlying machinery that emake does, but it has its own set of job types for its particular needs.


Every electrify build starts with an alpha job, which is a trivial placeholder marking the start of the list of jobs.


External jobs represent commands invoked by the electrified build tool which are distributed to the cluster. These are similar to emake’s rule jobs, except they will only ever run a single command, while rule jobs may run any number of commands.


Update jobs represent filesystem modifications made by commands invoked by the electrified build tool but not distributed to the cluster. These modifications are detected with the aid of electrifymon.


Every electrify build ends with an omega job, which, like the alpha job, is a trivial placeholder.


I hope you’ve found this post informative. Questions or feedback? Please feel free to comment below!

Maven 3 performance: why all the hubbub, bub?

First off let me say that I have never used Maven, and I honestly don’t know much about it. But I do know a lot about build performance and parallel builds, and I love to look at benchmarks, so naturally I was intrigued by this article on Dr. Dobb’s, which includes a comparison of performance between Maven 2 and Maven 3, the latest release; as well as a comparison between serial and parallel builds using the new parallel build feature in Maven 3.

The thing is, the improvement is not very impressive. Sonatype describes Maven 3 as “dramatically faster” than Maven 2, but according to the published benchmarks, Maven 3 shaves a paltry 5-10 seconds off the Maven 2 build time:

Project Maven 2 Maven 3 Improvement
Maven SCM Trunk 3:20 3:15 5s (2.5%)
“Corporate” 1:04 0:54 10s (15%)

The parallel build feature does better, executing the benchmark builds 1.35x faster (a 25% reduction in build time):

Project Serial Parallel (4 threads) Improvement
Maven SCM trunk 3:15 2:26 49s (25%)
“Corporate” 0:54 0:40 14s (25%)

That’s nothing to scoff at, I suppose, but consider that to obtain that 1.35x speedup, they used 4 threads of parallel execution — that’s the equivalent of make -j 4. With 4 threads of parallel execution you’ll see a 4x speedup, ideally. The benchmark builds here are small, so they are probably dominated by a few large jobs, but even so, with 4 threads you ought to be able to get at least a 2x speedup. For making the jump from serial to 4-way parallel builds, a 1.35x improvement is pretty disappointing.

At first I thought perhaps the author rushed when putting together the Dr. Dobb’s article, and just made a poor choice of benchmarks. But in fact the same benchmarks have been used in a Sonatype blog months before the Dr. Dobb’s article, and apparently in a Jfokus 2011 presentation by Dennis Lundberg.

Where’s the beef?

So what am I missing here? It seems there are only two possibilities:

  1. The performance improvements between Maven 2 and Maven 3 are actually impressive in some cases, and the Maven developers just couldn’t be bothered, over a period of several months, to find more compelling benchmarks; or
  2. The performance improvements between Maven 2 and Maven 3 are just not that impressive, and the published benchmarks are, sadly, representative of the best Maven 3 has to offer at this time.

I know where my money is on that bet.

How ElectricMake guarantees reliable parallel builds

Parallel execution is a popular technique for reducing software build length, and for good reason. These days, multi-core computers have become standard — even my laptop has four cores — so there’s horsepower to spare. And it’s “falling over easy” to implement: just slap a “-j” onto your make command-line, sit back and enjoy the benefits of a build that’s 2, 3 or 4 times faster than it used to be. Sounds great!

But then, inevitably, invariably, you run into parallel build problems: incomplete dependencies in your makefiles, tools that don’t adequately uniquify their temp file names, and any of a host of other things that introduce race conditions into your parallel build. Sometimes everything works great, and you get a nice, fast, correct build. Other times, your build blows up in spectacular fashion. And then there are the builds that appear to succeed, but in fact generate bogus outputs, because some command ran too early and used files generated in a previous instead of the current build.

This is precisely the problem ElectricMake was created to solve — it gives you fast, reliable parallel builds, regardless of how (im)perfect your makefiles and tools are. If the build works serially, it will work with ElectricMake, but faster. If you’ve worked with parallel builds for any length of time, you can probably appreciate the benefit of that guarantee.

But maybe you haven’t had much experience with parallel builds yourself, or maybe you have but like many people, you don’t believe this problem can actually be solved. In that case, perhaps some data will persuade you. Here’s a sample of open source projects that don’t build reliably in parallel using gmake:

For each, I did several trials with gmake at various levels of parallelism, to determine how frequently the parallel build fails. Then, I did the same build several times with emake and again measured the success rate. Here you can see the classic problem of parallel builds with gmake — works great at low levels of parallelism (or serially, the “degenerate” case of parallel!), but as you ratchet up the parallelism, the build gets less and less reliable. At the same time, you can see that emake is rock solid regardless of how much parallelism you use:

Parallel build success rates

The prize for this reliability? Faster builds, because you can safely exploit more parallelism. Where gmake becomes unreliable with -j 3 or -j 4, emake is reliable with any number of parallel jobs.

How ElectricMake guarantees reliable parallel builds

The technology that enables emake to ensure reliable parallel builds is called conflict detection. Although there are many nuances to its implementation, the concept is simple. First, track every modification to every file accessed by the build as a distinct version of the file. Then, for each job run during the build, track the files used and verify that the job accessed the same versions it would have had the build run serially. Any mismatch is considered a conflict. The offending job is discarded along with any filesystem modifications it made, and the job is rerun to obtain the correct result.

The versioned file system

At the heart of the conflict detection system is a data structure known as the versioned file system, in which emake records every version of every file used over the lifetime of the build. A version is added to the data structure every time a file is modified, whether that be a change to the content of the file, a change in the attributes (like ownership or access permissions), or the deletion of the file. In addition to recording file state, a version records the job which created it. For example, here’s what the version chain looks like for a file “foo” which initially does not exist, then is created by job A with contents “abc”, deleted by job C, and recreated by job E with contents “123”:


Jobs are the basic unit of work in emake. A job represents all the commands that must be run in order to build a single makefile target. In addition, every job has a serial order — the order in which the job would have run, had the build been run serially. The serial order of a job is dictated by the dependencies and structure of the makefiles that make up the build. Note that for a given build, the serial order is deterministic and unambiguous — even if the dependencies are incomplete, there is exactly one order for the jobs when the build is run serially.

With the serial order for every job in hand, deciding which file version should be used by a given job is simple: just find the version created by the job with the greatest serial order that precedes the job accessing the file. For example, using the version chain above (and assuming that the jobs’ names reflect their serial order), job B should use the version created by job A, while job D should see the file as non-existent, thanks to the version created by job C.

A job enters the completed state once all of its commands have been executed. At that point, any filesystem updates created by the job are integrated into the versioned filesystem, but, critically, they are not pushed to the real filesystem — that gives emake the ability to discard the updates if the job is later found to have conflicts.

Each job runs against a virtual filesystem called the Electric File System (EFS), rather than the real filesystem. The EFS serves several important functions: first, it is the means by which emake tracks file accesses. Second, it enables commands in the build to access file versions that exist in the versioned filesystem, but not yet on the real filesystem. Finally, it isolates simultaneously running jobs from one another, eliminating the possibility of crosstalk between commands.

Detecting conflicts

With all the data emake collects — every version of every file, and the relationship between every job — the actual conflict check is simple: for each file accessed by a job, compare the actual version to the serial version. The actual version is the version that was actually used when the job ran; the serial version is the version that would have been used, if the build had been run serially. For example, consider a job B which attempts to access a file foo. At the time that B runs, the version chain for foo looks like this:

Given that state, B will use the initial version of foo — there is no other option. The initial version is therefore the actual version used by job B. Later, job A creates a new version of foo:

Since job A precedes job B in serial order, the version created by job A is the correct serial version for job B. Therefore, job B has a conflict.

If a job is determined to be free of conflicts, the job is committed, meaning any filesystem updates are at last applied to the real filesystem. Any job that has a conflict is reverted — all versions created by the job are marked invalid, so subsequent jobs will not use them. The conflict job is then rerun in order to generate the correct result. The rerun job is committed immediately upon completion.

Conflict checks are carried out by a dedicated thread which inspects each job in strict serial order. That guarantees that a job is not checked for conflicts until after every job that precedes it in serial order has been successfully verified free of conflicts — without this guarantee, we can’t be sure that we know the correct serial version for files accessed by the job. Similarly, this ensures that the rerun job, if any, will use the correct serial versions for all files — so the rerun job is sure to be conflict free.

ElectricMake: reliable parallel builds

Conceptually, conflict detection is simple — keep track of every version of every file used in a build, then verify that each job used the correct version — but there are many details to its implementation. And in this article I’ve only covered the most basic implementation of conflict detection — after many years of experience and thousands of real-world builds we’ve tweaked the implementation, relaxing the simple definition of a conflict in specific cases in order to improve performance.

The benefit of conflict detection is simple too: reliable parallel builds, which in turn means shorter build times, regardless of how imperfect your makefiles are and how parallel-unsafe your toolchain may be.

Why is SCons so slow?

UPDATE: If you’re coming from Why SCons is not slow, you should read my response

A while back, I did a series of posts exploring the performance of SCons on builds of various sizes. The results were dismal: SCons demonstrated a classic O(n2) growth in runtime, meaning that the length of the build grew in proportion to the square of the number of files in the build, rather than linearly as one would hope. Naturally, that investigation and its results provoked a great deal of discussion at the time and since. Typically, SCons advocates fall back on one particular argument: “Sure, SCons may be slow,” they say, “but that’s the price you pay for a correct build.” Recently, Eric S. Raymond wrote an article espousing this same fundamental argument, with the addition of some algorithmic analysis intended to prove mathematically that a correct build, regardless of the build tool, must necessarily exhibit O(n2) behavior — a clever bit of circular logic, because it implies that any build tool that does not have such abyssmal performance must not produce correct builds!

Naturally, after spending nearly a decade developing a high-performance replacement for GNU make, I couldn’t let that statement stand. This post is probably going to be on the long side, so here’s the tl;dr summary:

  • You can guarantee correct builds with make, provided you follow best practices.
  • The worst-case runtime of any build tool if, of course, O(n2), but most, if not all, builds can be handled in O(n) time, without sacrificing correctness.
  • SCons’ performance problem is caused by design and implementation decisions in SCons, not some pathology of build structure.

What is required to ensure a correct build?

One of the fundamental tenents of the pro-SCons mythos is the idea that it is unique in its ability to guarantee correct builds. In reality, SCons is not doing anything particularly special in this regard. It’s true that by virtue of its design SCons makes it easier to get it right, but there’s nothing keeping you from enjoying the same assurances in make.

First: what is a correct build? Simply put, a correct build is one in which everything that ought to be built, is built. Note that by definition, a from-scratch build is correct, since everything is built in that case. So the question of “correct” or “incorrect” is really only relevant in regards to incremental builds.

So, what do we need in order to ensure a correct incremental build? Only three things, actually:

  1. A single, full-build dependency graph.
  2. Complete dependency information for every generated file.
  3. A reliable way to determine if a file is up-to-date relative to its inputs.

What SCons has done is made it more-or-less impossible, by design, to not have these three things. There is no concept like recursive make in the SCons world, so the only option is a single, full-build dependency graph. Likewise, SCons automatically scans input files in several programming languages to find dependency information. Finally, SCons uses MD5 checksums for the up-to-date check, which is a pretty darn reliable way to verify whether a given file needs to be rebuilt.

But the truth is, you can guarantee correct builds with make — you just have to adhere to long-standing best practices for make. First, you have to avoid using recursive make. Then, you need to add automatic dependency generation. The only thing that’s a little tricky is the up-to-date check: make is hardwired to use file timestamps, which can be spoofed, deliberately or accidentally — although to be fair, in most cases, timestamps are perfectly adequate. But even here, there’s a way out. You can use a smarter version of make that has a more sophisticated up-to-date mechanism, like ElectricMake or ClearMake. You can even shoehorn MD5 checksums into GNU make, if you like.

I can’t deny that SCons has made it easier to get correct builds. But the notion that it can’t be done with make is simply absurd.

What is the cost of a correct build?

Now we turn to the question of the cost of ensuring correctness. At its core, any build tool is just a collection of graph algorithms — first contructing the dependency graph, then traversing it to find and update out-of-date files. These algorithms have well-understood complexity, typically given as O(n + e), where n is the number of nodes in the graph, and e is the number of edges. It turns out that e is actually the dominant factor here, since it is at least equal to n, and at worst as much as n2. That means we can simplify the complexity to O(n + n2), or just O(n2).

Does this absolve SCons of its performance sins? Unfortunately it does not, because O(n2) is a worst-case bound — you should only expect O(n2) behavior if you’ve got a build that has dependencies between every pair of files. Think about that for a second. A dependency between every. pair. of. files. Here’s what that would look like in makefile syntax:

all: foo bar foo.c bar.c foo.h bar.h
foo:     bar foo.c bar.c foo.h bar.h
bar:         foo.c bar.c foo.h bar.h
foo.c:             bar.c foo.h bar.h
bar.c:                   foo.h bar.h
foo.h:                         bar.h

It’s ridiculous, right? I don’t know about you, but I’ve certainly never seen a build that does anything even remotely like that. In particular, the builds I used in my benchmarks don’t look like that. Fortunately, those builds are small and simple enough that we can directly count the number of edges in the dependency graph. For example, the smallest build in my tests consisted of:

2,000 C sources
+ 2,004 headers
+ 2,000 objects
+ 101 libraries
+ 100 executables

6,205 total files

So we have about 6,000 nodes in the graph, but how many edges does the graph contain? Lucky for us, SCons will print the complete dependency graph if we invoke it with scons –tree=all:

  | +-d1_0/SConstruct
  | +-d1_0/f00000_sconsbld_d1_0
  | | +-d1_0/f00000_sconsbld_d1_0.o
  | | | +-d1_0/f00000_sconsbld_d1_0.c
  | | | +-d1_0/lup001_sconsbld_d1_0/f00000_sconsbld_d1_0.h

The raw listing contains about 35,000 lines of text, but that includes duplicates and non-dependency information like filesystem structure. Filter that stuff out and you can see the graph contains only about 12,000 dependencies. That’s a far cry from the 1,800,000 or so you would expect if this truly were a “worst-case” build. It’s clear, in fact, that the number of edges is best described as O(n).

Although I don’t know how (or even if it’s possible) to prove that this is the general case, it does make a certain intuitive sense: far from being strongly-connected, most of the nodes in a build dependency graph have just one or two edges. Each C source file, for example, has just one outgoing edge, to the object file generated from that source. Each object file has just one outgoing edge too, to the library or executable the object is part of. Sure, libraries and headers probably have more edges, since they are used by multiple executables or objects, but the majority of the stuff in the graph is going to fall into the “small handful of edges” category.

Now, here’s the $64,000 question: if the algorithms in a build tool scale in proportion to the number of edges in the dependency graph, and we’ve just shown that the dependency graph in question has O(n) edges, why does SCons use O(n2) time to execute the build?

Why is SCons so slow?

SCons’ O(n2) performance stems from its graph traversal implementation. Essentially, SCons scans the entire dependency graph each time it is looking for a file to update. n scans of a graph with O(n) nodes and edges equals an O(n2) graph traversal. There’s no mystery here. In fact, the SCons developers are clearly aware of this deficiency, as described on their wiki:

It’s worth noting that the Jobs module calls the Taskmaster once for each node to be processed (i.e., it’s O(n)) and the Taskmaster has an amortized performance of O(n) each time it’s called. Thus, the overall time is O(n^2).

But despite recognizing this flaw, they severely misjudged its impact, because they go on to state that it requires a “pathological” dependency graph in order to elicit this worst-case behavior from SCons. As we’ve shown here and in previous posts, even a terribly mundane dependency graph elicits O(n2) behavior from SCons. I shudder to think what SCons would do with a truly pathological dependency graph!

Obviously the next question is: why does SCons do this? That’s not quite as easy for me to explain, as an outside observer. To the best of my understanding, they rescan the graph just in case new dependencies are added to the dependency graph while evaluating a node in the graph — remember, in SCons the commands to update a file are expressed in Python, so they can easily manipulate the dependency graph even while the build is running.

Is it really necessary to rescan the dependency graph over and over? I don’t think so. In fact, make is proof that it is not necessary. I think there are two ways that SCons could address this problem: first, it could adopt GNU make’s convention of partitioning the build into distinct phases, one that updates dependency information, and a second that actually executes the build. In GNU make, that strategy allows for the introduction of new dependency information, while imposing only a one-time O(n) cost for restarting the make process if any new dependencies are found.

Alternatively, SCons could probably be made smarter about when a full rescan is required. Most of the time, even if new dependencies are added to the graph, they are added to the node being evaluated, not to nodes that were already visited. That is, when you scan a source file for implicit dependencies, you find the dependencies for that file not for other files in the build (duh). So most of the time, a full rescan is massive overkill.

The final word…?

Hopefully this is my last post on the subject of SCons performance. It is clear to me that SCons does not scale to large projects, and that the problem stems from design and implementation decisions in SCons, rather than some pathology in the build itself. You can get comparable guarantees of correctness from make, if you’re willing to invest the time to do things the right way. The payoff is a build system that is not only correct but has vastly better performance than SCons as your project grows. Why wouldn’t you want that?

What’s new in ElectricAccelerator 5.4.0

This month, Electric Cloud announced the release of ElectricAccelerator 5.4. This version adds a lot of great new features, including support for GNU Make’s .SECONDEXPANSION feature and the use of $(eval) in rule bodies, and compatibility with Cygwin 1.7.7. In addition to those long-awaited improvements, here are the things that I’m most excited about in this release:

New cluster utilization reports

Accelerator 5.4 includes two new reports designed to give you greater insight into the load on and utilization of your cluster: the Cluster Utilization report and the Sealevel report:

The Cluster Utilization report shows, over the course of a typical day, the average number of builds running and the average combined agent demand from all running builds. The Sealevel report shows the raw agent demand data, plotted over the course of a day. The colored bands correspond to various cluster sizes, including the current cluster size and several hypothetical sizes, so you can see at a glance how large you need to make the cluster in order to satisfy all the agent requests. The percentages on the right side of the graph indicate the portion of agent requests that are left unsatisfied with a cluster of the given size. In the example above, all but 1% of agent requests would be satisfied if the cluster had 40 agents.

Reduced directory creation conflicts

Raise your hand if you’ve ever seen this pattern in a makefile:

%.o: %.c
        @mkdir -p $(dir $@)
        @$(COMPILE.c) -o $@ $<

It’s a common way to ensure the output directory exists before trying to create a file in it. Unfortunately, with a strict application of Accelerator’s conflict detection algorithm, this pattern causes numerous conflicts and poor performance when the build is run without an up-to-date history file. In Accelerator 5.4.0, we improved the algorithm so that this common case is no longer considered a conflict. If you always run with a good history file, this change will not be helpful to you. But sometimes that’s not possible — for example, if you’re building third-party code that’s just gotten a major update — then you’re going to really love this improvement. The Android source code is a perfect example: a from-scratch no-history build of the Gingerbread base used to take 144 minutes. Now it runs in just 22 minutes on the same hardware — 6.5x faster.

New Linux sandbox implementation

The last feature I want to mention here is the new sandbox implementation for Linux. The sandbox is the means by which Accelerator is able to present a different view of the filesystem, from a different point of time during the build, to each of the jobs running concurrently on a given agent host. Without the sandbox, it would be impossible on Linux to simultaneously represent a given file as existent to one job, and non-existent to another.

In previous versions of Accelerator, the Linux sandbox implementation was effective, but ultimately limited in its capabilities. Chief among those limitations was an inability to interoperate with autofs 5.x. There were several workarounds available, but each of those in turn had its own shortcomings.

Accelerator 5.4 uses a different underlying technology to implement the sandbox component: lofs, the loopback filesystem. This is a concept borrowed from Solaris, which has had a vendor-supplied version for years; Linux has nothing that matches the depth of functionality provided by Solaris, so we wrote our own. The net result of this effort is that the limitations of the previous implementation have been entirely eliminated. In particular, Accelerator 5.4 can interoperate with autofs 5.x without the need for any workarounds or awkward configuration.


It’s been a long time in coming, but I think it was well worth the wait. I’m very proud to have been part of this product release, and I’m thrilled with the work my team has put into it.

Accelerator 5.4 is available immediately for current customers. New customers should contact