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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) [2] [22] J2SE 1.2: Use of a generational collector. J2SE 1.3: Just-in-time compiling by HotSpot.
This list of JVM Languages comprises notable computer programming languages that are used to produce computer software that runs on the Java virtual machine (JVM). Some of these languages are interpreted by a Java program, and some are compiled to Java bytecode and just-in-time (JIT) compiled during execution as regular Java programs to improve performance.
The project is continuously evolving. The list of supported programming languages is updated approximately once per year, following market trends. Users can also submit improved solutions to any of the problems or suggest testing methodology refinement. [9]
C, C++, Fortran, Java; MPI: Performance and memory profiler. Proprietary freeware perf tools: Linux kernel 2.6.31+ Sampling profiler with support of hardware events on several architectures. GPL Performance Application Programming Interface (PAPI) Various Library for hardware performance counters on modern microprocessors. Pin by Intel
Comparison of Java and .NET platforms ALGOL 58's influence on ALGOL 60; ALGOL 60: Comparisons with other languages; Comparison of ALGOL 68 and C++; ALGOL 68: Comparisons with other languages; Compatibility of C and C++; Comparison of Pascal and Borland Delphi; Comparison of Object Pascal and C; Comparison of Pascal and C; Comparison of Java and C++
Java-based Template Engine, originally focusing on dynamic web page generation with MVC software architecture GeoApi: Set of Java language programming interfaces for geospatial applications. GeoTools: Java library that provides tools for geospatial data. GlassFish: Application server and official reference implementation for Servlets 3.0 ...
Memory access performance and the efficiency of 'boxed' value access are a major area to be addressed by these features. 'Value Type' features and 'Generic specialization' (when applied to lists or collections) reduce memory usage, but more importantly avoid pointer indirection which typically causes a cache miss.
In computer programming, profile-guided optimization (PGO, sometimes pronounced as pogo [1]), also known as profile-directed feedback (PDF) [2] or feedback-directed optimization (FDO), [3] is the compiler optimization technique of using prior analyses of software artifacts or behaviors ("profiling") to improve the expected runtime performance of the program.