Java performance

Virtual machine optimization methods

Many optimizations have improved the performance of the JVM over time. However, although Java was often the first Virtual machine to implement them successfully, they have often been used in other similar platforms as well.

Just-in-time compiling

Early JVMs always interpreted Java bytecodes. This had a large performance penalty of between a factor 10 and 20 for Java versus C in average applications. To combat this, a just-in-time (JIT) compiler was introduced into Java 1.1. Due to the high cost of compiling, an added system called HotSpot was introduced in Java 1.2 and was made the default in Java 1.3. Using this framework, the Java virtual machine continually analyses program performance for hot spots which are executed frequently or repeatedly. These are then targeted for optimizing, leading to high performance execution with a minimum of overhead for less performance-critical code. Some benchmarks show a 10-fold speed gain by this means. However, due to time constraints, the compiler cannot fully optimize the program, and thus the resulting program is slower than native code alternatives.

Adaptive optimizing

Adaptive optimizing is a method in computer science that performs dynamic recompilation of parts of a program based on the current execution profile. With a simple implementation, an adaptive optimizer may simply make a trade-off between just-in-time compiling and interpreting instructions. At another level, adaptive optimizing may exploit local data conditions to optimize away branches and use inline expansion.

A Java virtual machine like HotSpot can also deoptimize code formerly JITed. This allows performing aggressive (and potentially unsafe) optimizations, while still being able to later deoptimize the code and fall back to a safe path.

Garbage collection

The 1.0 and 1.1 Java virtual machines (JVMs) used a mark-sweep collector, which could fragment the heap after a garbage collection. Starting with Java 1.2, the JVMs changed to a generational collector, which has a much better defragmentation behaviour. Modern JVMs use a variety of methods that have further improved garbage collection performance.

Compressed Oops

Compressed Oops allow Java 5.0+ to address up to 32 GB of heap with 32-bit references. Java does not support access to individual bytes, only objects which are 8-byte aligned by default. Because of this, the lowest 3 bits of a heap reference will always be 0. By lowering the resolution of 32-bit references to 8 byte blocks, the addressable space can be increased to 32 GB. This significantly reduces memory use compared to using 64-bit references as Java uses references much more than some languages like C++. Java 8 supports larger alignments such as 16-byte alignment to support up to 64 GB with 32-bit references.

Split bytecode verification

Before executing a class, the Sun JVM verifies its Java bytecodes (see bytecode verifier). This verification is performed lazily: classes' bytecodes are only loaded and verified when the specific class is loaded and prepared for use, and not at the beginning of the program. (Note that other verifiers, such as the Java/400 verifier for IBM iSeries (System i), can perform most verification in advance and cache verification information from one use of a class to the next.) However, as the Java class libraries are also regular Java classes, they must also be loaded when they are used, which means that the start-up time of a Java program is often longer than for C++ programs, for example.

A method named split-time verification, first introduced in the Java Platform, Micro Edition (J2ME), is used in the JVM since Java version 6. It splits the verification of Java bytecode in two phases:

In practice this method works by capturing knowledge that the Java compiler has of class flow and annotating the compiled method bytecodes with a synopsis of the class flow information. This does not make runtime verification appreciably less complex, but does allow some shortcuts.

Escape analysis and lock coarsening

Java is able to manage multithreading at the language level. Multithreading is a method allowing programs to perform multiple processes concurrently, thus producing faster programs on computer systems with multiple processors or cores. Also, a multithreaded application can remain responsive to input, even while performing long running tasks.

However, programs that use multithreading need to take extra care of objects shared between threads, locking access to shared methods or blocks when they are used by one of the threads. Locking a block or an object is a time-consuming operation due to the nature of the underlying operating system-level operation involved (see concurrency control and lock granularity).

As the Java library does not know which methods will be used by more than one thread, the standard library always locks blocks when needed in a multithreaded environment.

Before Java 6, the virtual machine always locked objects and blocks when asked to by the program, even if there was no risk of an object being modified by two different threads at once. For example, in this case, a local vector was locked before each of the add operations to ensure that it would not be modified by other threads (vector is synchronized), but because it is strictly local to the method this is needless:

Starting with Java 6, code blocks and objects are locked only when needed, so in the above case, the virtual machine would not lock the Vector object at all.

Since version 6u23, Java includes support for escape analysis.

Register allocation improvements

Before Java 6, allocation of registers was very primitive in the client virtual machine (they did not live across blocks), which was a problem in CPU designs which had fewer processor registers available, as in x86s. If there are no more registers available for an operation, the compiler must copy from register to memory (or memory to register), which takes time (registers are significantly faster to access). However, the server virtual machine used a color-graph allocator and did not have this problem.

An optimization of register allocation was introduced in Sun's JDK 6; it was then possible to use the same registers across blocks (when applicable), reducing accesses to the memory. This led to a reported performance gain of about 60% in some benchmarks.

Class data sharing

Class data sharing (called CDS by Sun) is a mechanism which reduces the startup time for Java applications, and also reduces memory footprint. When the JRE is installed, the installer loads a set of classes from the system JAR file (the JAR file holding all the Java class library, called rt.jar) into a private internal representation, and dumps that representation to a file, called a "shared archive". During subsequent JVM invocations, this shared archive is memory-mapped in, saving the cost of loading those classes and allowing much of the JVM's metadata for these classes to be shared among multiple JVM processes.

The corresponding improvement in start-up time is more obvious for small programs.

History of performance improvements

Apart from the improvements listed here, each release of Java introduced many performance improvements in the JVM and Java application programming interface (API).

JDK 1.1.6: First just-in-time compilation (Symantec's JIT-compiler)

J2SE 1.2: Use of a generational collector.

J2SE 1.3: Just-in-time compiling by HotSpot.

J2SE 1.4: See here, for a Sun overview of performance improvements between 1.3 and 1.4 versions.

Java SE 5.0: Class data sharing

Java SE 6:

Other improvements:

See also 'Sun overview of performance improvements between Java 5 and Java 6'.

Java SE 6 Update 10

Java 7

Several performance improvements have been released for Java 7: Future performance improvements are planned for an update of Java 6 or Java 7:

Comparison to other languages

Objectively comparing the performance of a Java program and an equivalent one written in another language such as C++ needs a carefully and thoughtfully constructed benchmark which compares programs completing identical tasks. The target platform of Java's bytecode compiler is the Java platform, and the bytecode is either interpreted or compiled into machine code by the JVM. Other compilers almost always target a specific hardware and software platform, producing machine code that will stay virtually unchanged during execution. Very different and hard-to-compare scenarios arise from these two different approaches: static vs. dynamic compilations and recompilations, the availability of precise information about the runtime environment and others.

Java is often compiled just-in-time at runtime by the Java virtual machine, but may also be compiled ahead-of-time, as is C++. When compiled just-in-time, in micro-benchmarks its performance is generally:

Program speed

Benchmarks often measure performance for small numerically intensive programs. In some rare real-life programs, Java out-performs C. One example is the benchmark of Jake2 (a clone of Quake II written in Java by translating the original GPL C code). The Java 5.0 version performs better in some hardware configurations than its C counterpart. While it's not specified how the data was measured (for example if the original Quake II executable compiled in 1997 was used, which may be considered bad as current C compilers may achieve better optimizations for Quake), it notes how the same Java source code can have a huge speed boost just by updating the VM, something impossible to achieve with a 100% static approach.

For other programs, the C++ counterpart can, and usually does, run significantly faster than the Java equivalent. A benchmark performed by Google in 2011 showed a factor 10 between C++ and Java. At the other extreme, an academic benchmark performed in 2012 with a 3D modelling algorithm showed the Java 6 JVM being from 1.09 to 1.91 times slower than C++ under Windows.

Some optimizations that are possible in Java and similar languages may not be possible in certain circumstances in C++:

The JVM is also able to perform processor specific optimizations or inline expansion. And, the ability to deoptimize code already compiled or inlined sometimes allows it to perform more aggressive optimizations than those performed by statically typed languages when external library functions are involved.

Results for microbenchmarks between Java and C++ highly depend on which operations are compared. For example, when comparing with Java 5.0:

Multi-core performance

The scalability and performance of Java applications on multi-core systems is limited by the object allocation rate. This effect is sometimes called an "allocation wall". However, in practice, modern garbage collector algorithms use multiple cores to perform garbage collection, which to some degree alleviates this problem. Some garbage collectors are reported to sustain allocation rates of over a gigabyte per second, and there exist Java-based systems that have no problems scaling to several hundreds of CPU cores and heaps sized several hundreds of GB.

Automatic memory management in Java allows for efficient use of lockless and immutable data structures that are extremely hard or sometimes impossible to implement without some kind of a garbage collection. Java offers a number of such high-level structures in its standard library in the java.util.concurrent package, while many languages historically used for high performance systems like C or C++ are still lacking them.

Startup time

Java startup time is often much slower than many languages, including C, C++, Perl or Python, because many classes (and first of all classes from the platform Class libraries) must be loaded before being used.

When compared against similar popular runtimes, for small programs running on a Windows machine, the startup time appears to be similar to Mono's and a little slower than .NET's.

It seems that much of the startup time is due to input-output (IO) bound operations rather than JVM initialization or class loading (the rt.jar class data file alone is 40 MB and the JVM must seek much data in this big file). Some tests showed that although the new split bytecode verification method improved class loading by roughly 40%, it only realized about 5% startup improvement for large programs.

Albeit a small improvement, it is more visible in small programs that perform a simple operation and then exit, because the Java platform data loading can represent many times the load of the actual program's operation.

Starting with Java SE 6 Update 10, the Sun JRE comes with a Quick Starter that preloads class data at OS startup to get data from the disk cache rather than from the disk.

Excelsior JET approaches the problem from the other side. Its Startup Optimizer reduces the amount of data that must be read from the disk on application startup, and makes the reads more sequential.

In November 2004, Nailgun, a "client, protocol, and server for running Java programs from the command line without incurring the JVM startup overhead" was publicly released. introducing for the first time an option for scripts to use a JVM as a daemon, for running one or more Java applications with no JVM startup overhead. The Nailgun daemon is insecure: "all programs are run with the same permissions as the server". Where multi-user security is needed, Nailgun is inappropriate without special precautions. Scripts where per-application JVM startup dominates resource use, see one to two order of magnitude runtime performance improvements.

Memory use

Java memory use is much higher than C++'s memory use because:

In most cases a C++ application will consume less memory than an equivalent Java application due to the large overhead of Java's virtual machine, class loading and automatic memory resizing. For programs in which memory is a critical factor for choosing between languages and runtime environments, a cost/benefit analysis is needed.

Trigonometric functions

Performance of trigonometric functions is bad compared to C, because Java has strict specifications for the results of mathematical operations, which may not correspond to the underlying hardware implementation. On the x87 floating point subset, Java since 1.4 does argument reduction for sin and cos in software, causing a big performance hit for values outside the range.

Java Native Interface

The Java Native Interface invokes a high overhead, making it costly to cross the boundary between code running on the JVM and native code. Java Native Access (JNA) provides Java programs easy access to native shared libraries (dynamic-link library (DLLs) on Windows) via Java code only, with no JNI or native code. This functionality is comparable to Windows' Platform/Invoke and Python's ctypes. Access is dynamic at runtime without code generation. But it has a cost, and JNA is usually slower than JNI.

User interface

Swing has been perceived as slower than native widget toolkits, because it delegates the rendering of widgets to the pure Java 2D API. However, benchmarks comparing the performance of Swing versus the Standard Widget Toolkit, which delegates the rendering to the native GUI libraries of the operating system, show no clear winner, and the results greatly depend on the context and the environments.

In most cases Java suffers greatly from its need to copy image data from one place in memory to another before rendering it to the screen. C++ can usually avoid this large overhead by accessing memory directly. The developers of Java have attempted to overcome this limitation with certain so-called "unsafe" direct memory access classes. However, those attempts fall far short of what C++ natively offers. For example, two major Java OpenGL implementations suffer tremendously from this data duplication problem which is difficult, if not impossible, to avoid with Java.

Use for high performance computing

Some people believe that Java performance for high performance computing (HPC) is similar to Fortran on compute-intensive benchmarks, but that JVMs still have scalability issues for performing intensive communication on a grid computing network.

However, high performance computing applications written in Java have won benchmark competitions. In 2008, and 2009, an Apache Hadoop (an open-source high performance computing project written in Java) based cluster was able to sort a terabyte and petabyte of integers the fastest. The hardware setup of the competing systems was not fixed, however.

In programming contests

Programs in Java start slower than those in other compiled languages. Thus, some online judge systems, notably those hosted by Chinese universities, use longer time limits for Java programs to be fair to contestants using Java.