Make Java fast! Performance tuning Java

Make Java fast! Performance tuning Java

Learn how to optimise JVM and JIT compiler performance for better execution speed, memory usage, and resource utilisation in your Java applications—and how to check your results.

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JVM optimisation enhances the performance and efficiency of Java applications that run on the Java virtual machine. It involves techniques and strategies aimed at improving execution speed, reducing memory usage, and optimising resource utilisation.

One aspect of JVM optimisation involves memory management since it includes configuring the JVM's memory allocation settings, such as heap sizes and garbage collector parameters. The goal is to ensure efficient memory usage and minimise unnecessary object creation and memory leaks. Additionally, optimising the JVM's Just-in-Time (JIT) compiler is crucial.

By analysing code patterns, identifying hotspots, and applying optimisations like inlining and loop unrolling (see below), the JIT compiler dynamically translates frequently executed bytecode into native machine code, resulting in faster execution.

Another critical aspect of JVM optimisation is thread management. Efficient utilisation of threads is vital for concurrent Java applications. Optimising thread usage involves minimising contention, reducing context switching, and effectively employing thread pooling and synchronisation mechanisms.

Finally, fine-tuning JVM parameters, such as heap size and thread-stack size, can optimise the JVM's behavior for better performance. Profiling and analysis tools are utilised to identify performance bottlenecks, hotspots, and memory issues, enabling developers to make informed optimisation decisions. JVM optimisation aims to achieve enhanced performance and responsiveness in Java applications by combining these techniques and continuously benchmarking and testing the application.

Optimising the JIT compiler

Optimising the JVM's Just-in-Time compiler is a crucial aspect of Java performance optimisation. The JIT compiler is responsible for dynamically translating frequently executed bytecode into native machine code, improving the performance of Java applications.

The JIT compiler works by analysing the bytecode of Java methods at runtime and identifying hotspots, which are frequently executed code segments. Once it identifies a hotspot, the JIT compiler applies various optimisation techniques to generate highly optimised native machine code for that code segment. Standard JIT optimisation techniques include the following:

  • Inlining: The JIT compiler may decide to inline method invocations, which means replacing the method call with the actual code of the method. Inlining reduces method invocation overhead and improves execution speed by eliminating the need for a separate method call.
  • Loop unrolling: The JIT compiler may unroll loops by replicating loop iterations and reducing the number of loop control instructions. This technique reduces loop overhead and improves performance, particularly in cases where loop iterations are known or can be determined at runtime.
  • Eliminate dead code: The JIT compiler can identify and eliminate code segments that are never executed, known as dead code. Removing dead code reduces unnecessary computations and improves the overall speed of execution.
  • Constant folding: The JIT compiler evaluates and replaces constant expressions with their computed values at compile-time. Constant folding reduces the need for runtime computations and can improve performance, especially with frequently used constants.
  • Method specialisation: The JIT compiler can generate specialised versions of methods based on their usage patterns. Specialised versions are optimised for specific argument types or conditions, improving performance for specific scenarios.

These are just a few examples of JIT optimisations. The JIT compiler continuously analyses an application's execution profile and dynamically applies optimisations to improve performance. By optimising the JIT compiler, developers can achieve significant performance gains in Java applications running on the JVM.

Optimising the Java garbage collector

Optimising the Java garbage collector (GC) is an essential aspect of JVM optimisation that focuses on improving memory management and reducing the impact of garbage collection on Java application performance. The garbage collector is responsible for reclaiming memory occupied by unused objects. Here are some of the ways developers can optimise garbage collection:

  • Choose the right garbage collector: The JVM offers a variety of garbage collectors that implement different garbage collection algorithms. There are Serial, Parallel, and Concurrent Mark Sweep (CMS) garbage collectors. Newer variants include G1 (Garbage-First) and ZGC (Z Garbage Collector). Each one has its strengths and weaknesses. Understanding the characteristics of your application, such as its memory-usage patterns and responsiveness requirements, will help you select the most effective garbage collector.
  • Tune GC parameters: The JVM provides configuration parameters that can be adjusted to optimise the garbage collector's behavior. These parameters include heap size, thresholds for triggering garbage collection, and ratios for generational memory management. Tuning JVM parameters can help balance memory utilisation and garbage collection overhead.
  • Generational memory management: Most garbage collectors in the JVM are generational, dividing the heap into young and old generations. Optimising generational memory management involves adjusting the size of each generation, setting the ratio between them, and optimising the frequency and strategy of garbage collection cycles for each generation. This helps promote efficient object allocation and short-lived object collection.
  • Minimise object creation and retention: Excessive object creation and unnecessary object retention can increase memory usage and lead to more frequent garbage collection. Optimising object creation involves reusing objects, employing object pooling techniques, and minimising unnecessary allocations. Reducing object retention involves identifying and eliminating memory leaks, such as unreferenced objects that are unintentionally kept alive.
  • Concurrent and parallel collection: Some garbage collectors, like CMS and G1, support concurrent and parallel garbage collection. Enabling concurrent garbage collection allows the application to run concurrently with the garbage collector, reducing pauses and improving responsiveness. Parallel garbage collection utilises multiple threads to perform garbage collection, speeding up the process for large heaps.
  • GC logging and analysis: Monitoring and analysing garbage collection logs and statistics can provide insight into the behavior and performance of the garbage collector. It helps identify potential bottlenecks, long pauses, or excessive memory usage. We can use this information to fine-tune garbage collection parameters and optimisation strategies.

By optimising garbage collection, developers can achieve better memory management, reduce garbage collection overhead, and improve application performance. However, it's important to note that optimising garbage collection is highly dependent on the specific characteristics and requirements of the application; it often involves a balance between memory utilisation, responsiveness, and throughput.

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