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About Azul Systems

Azul Systems
Azul Systems is the award-winning leader in Java runtime solutions. Azul has targeted and solved the most difficult Java performance problems — issues that have plagued the Java community for years. Most of Azul’s innovations are available in Zing, the only Java runtime (JVM) that has solved the Java garbage collection issue

Faster JVM Application Warm Up With Zing

The Java Virtual Machine (JVM) provides a managed runtime environment for the safe deployment of applications with performance that can often exceed that of natively compiled languages like C and C++.  Memory management with garbage collection and adaptive compilation through the use of just in time (JIT) compilation are the two most prominent features.

Although the use of bytecodes and JIT compilation can deliver better peak performance, the warm-up time required to reach that level can be problematic for certain classes of application.

In this post, we’ll look at a set of technologies Azul has developed as part of the Zing JVM to address these limitations.

To start with, let’s look at a typical graph of application performance when running on the JVM.

JVM Application

This graph is not ideal, as the application starts with reduced performance and the JVM takes time to reach its full potential.  The graph can be divided into three distinct sections; let’s look at what is happening inside the JVM for each of these.

  • When the application starts, the JVM must load and initialize the necessary classes.  Having done that, the JVM starts execution at the main() entry point.  Since the JVM is a Virtual Machine, it does not use the same instruction set as the physical machine on which it is running.  It is therefore necessary to convert the bytecodes of the class files to the instruction set of the physical CPU.  This is referred to as bytecode interpretation.  This must be repeated for every bytecode that is executed, which leads to much lower performance than a natively compiled application.  This was in large part responsible for Java’s reputation for being slow when it was first released.

    Interpreted mode is shown in yellow on the graph above.
  • To alleviate the problems of running in interpreted mode, the JVM records statistics internally of how frequently each method is called.  By doing this, it is able to identify hot spots (hence the name of the Oracle JVM) in the code for methods that are called repeatedly, such as in a long-running loop.  When the method call count reaches a defined threshold, the JVM passes the method to an internal compiler, referred to as a Just in Time compiler (often called a JIT).

    The compiler used by the JVM at this stage is called C1 (previously, this was also referred to as the client compiler).  The C1 JIT is designed to generate code as quickly as possible in order to rapidly improve the performance of these methods.  To do this, C1 will only apply the simplest optimizations that do not require additional profiling data or a long time to generate.

    This is shown as the green section on the graph above with performance improving gradually as more methods are compiled.

    As this code runs, the JVM will collect comprehensive profiling data about how the method is being used and how the code is executing.
  • At a second threshold of how many times a method is called, the JVM will recompile the method using a different JIT compiler.  In the case of Zing, this is the Falcon JIT, based on the open-source LLVM project.  The default OpenJDK second-level JIT compiler is C2, which is very old and hard to enhance.

    Falcon is a much more sophisticated compiler than C1.  It uses the profiling data gathered during the execution of the C1 generated code as well as other internal data from the JVM to apply the maximum level of optimizations to the code that it generates.  This is the blue section of the graph and performance will eventually reach the maximum level once all frequently used methods have been compiled.  At this point, the application is considered to have warmed-up.

Now that we understand the way that JIT compilation works in the JVM, what can be done to reduce its impact on application startup performance?  Azul has developed two technologies to give the Zing JVM the ability to mitigate the warm-up effect.

A common suggestion for how to solve this problem is to let the application run until all frequently used methods have been JIT compiled, then have the JVM write the compiled code to a file.  When the application is restarted, the previously compiled code can be reloaded, and the application will run at the speed it did before it stopped.

This sounds like a good solution but has two significant drawbacks:

  • Although the code was compiled for the running application, there is no guarantee it will still be valid when the JVM is restarted.  A good example of why this will not work is the use of assertions.  If an application is run with assertions disabled the JIT will eliminate the relevant sections of code.  If the application is then restarted with assertions enabled and uses the previously compiled code, the assertions will be missing.
  • There is a precise definition of how the JVM must work, which is the JVM Specification.  This is included in the Java SE specification created as part of the relevant JSR under the JCP.  This defines that specific tasks must be carried out when the JVM runs an application.  Classes must be explicitly loaded and initialised before they can be used.  Again, if the previously compiled code were used this could invalidate the correct operation of the JVM.

Azul’s ReadyNow! technology takes a different approach that ensures full correctness of both the code being executed and the startup sequence of the JVM.

To achieve this ReadyNow! records a profile of a running application.  The profile can be taken at any time so users can decide when their application is performing at the level that they require.  Multiple profile snap-shots can be taken so that a user can select the desired profile to use when restarting the application.

The profile records five pieces of data:

  1. A list of all the classes that have been loaded.
  2. A list of all the classes that have been initialized.
  3. The profiling data gathered during execution of C1 JIT compiled code.
  4. Compilations performed by both the C1 and Falcon JITs.
  5. A list of speculative optimizations that failed and resulted in deoptimizations of code.

When the application is started again, this data is used as advanced knowledge for the JVM to perform the following steps:

  • Load all bootstrap and system classes listed in the profile.
  • Initialize a safe subset of those loaded classes.  Classes that are considered safe are ones permitted by the JMV specification.
  • Other classes in the profile will be loaded as soon as the required classloader is identified.  This is necessary because of the dynamic nature of the Java platform, as described earlier.
  • The profiling and speculative optimization data is used to compile the required methods using the Falcon JIT. 

All this happens before the application starts execution at the main() entry point.

The effect of this is that when the application starts execution almost all of the hot methods have been compiled with the Falcon JIT.  By using the profiling data, the code can be heavily optimized, and speculative optimizations that are known to work are used (ones that don’t can also be avoided).  Performance starts at a level very close to that when the profile was collected.  Due to some restrictions in how this process works the application typically needs only a few transactions to bring it up to full speed.

This approach does, however, have an impact. The JVM has considerably more work to do in advance of when the application can start processing transactions. 

To reduce this effect, Azul has developed Compile Stashing

As we’ve already seen, it is not possible to simply save compiled code and then reload it when restarting an application.  However, it is possible to save the compiled code and use it, in effect, as a cache.

Bytecodes for methods are combined with saved profiling data so they can be converted into an intermediate representation (IR) used by the compiler.  As the code is compiled, the JIT will make calls to the JVM to help it make decisions about optimizations that it can use.  For example, to determine whether a method can be inlined the JIT must first establish whether the method can be de-virtualized, which requires a query to the JVM.  Once the JIT has completed an analysis of the method, it can compile it with the maximum level of optimization.

This process is fully deterministic.  Given the same method bytecodes and profiling data as input and the same set of queries to the JVM, the output from the JIT compiler will always be identical.

Compile Stashing complements ReadyNow!  In addition to recording a profile, the native code of the currently compiled methods are also written to a file, as well as the queries and responses for the VM callbacks.  When the application is started again, ReadyNow! loads and initializes the classes it can, based on the profile as before.  However, the saved compiled methods are now used as a cache to reduce the need for explicit compilation.  The diagram of the flow of operations is shown below:

JVM Application

ReadyNow! uses the combination of the IR for the method’s bytecodes and queries to the VM used during compilation to determine whether the stored compiled code matches.  If it does, the code can be returned from the Compile Stash.  If, for whatever reason, the inputs do not match the compilation request can be passed to the Falcon JIT, as before.  It is important to note that using this technique does not invalidate any of the requirements of the JVM specification regarding initialization of the application.

Tests have shown that, using Compile Stashing, the compile time required by ReadyNow! can be reduced by up to 80%, as well as reducing the CPU load by up to 60%.

As you can see, ReadyNow! and Compile Stashing address the issue of application warm-up time by recording class loading and profiling data, speculative optimizations that did not work and compiled code.   Using all these pieces when restarting an application can lead to a massive reduction in the time and CPU load required for the application to reach the optimum performance level.

Zing is the JVM that starts fast, stays fast and goes faster.

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