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The Next Wave of Enterprise Performance with Java, POWER Systems and NVIDIA GPUs

The Java ecosystem is the leading enterprise software development platform, with widespread industry support and deployment on platforms like the IBM WebSphere Application Server product family. Java provides a powerful object-oriented programming language with a large developer ecosystem and developer-friendly features like automated memory management, program safety, security and runtime portability, and high performance features like just-in-time (JIT) compilation.

Java application developers face increasingly complex challenges, with big data and analytics workloads that require next generation performance. Big data pushes the scale of the problem to a new level with multiple hundreds of gigabytes of information common in these applications, while analytics drive the need for higher computation speeds. The Java platform has evolved by adding developer support for simpler parallel programming via the fork/join framework and concurrent collection APIs. Most recently, Java 8 adds support for lambda expressions, which can simplify the creation of highly parallel applications using Java.

IBM's new Power S824L is a data processing powerhouse that integrates the NVIDIA Tesla Accelerated Computing Platform (Tesla GPUs and enabling software) with IBM’s POWER8 processor.
IBM’s new Power S824L is a data processing powerhouse that integrates the NVIDIA Tesla Accelerated Computing Platform (Tesla GPUs and enabling software) with IBM’s POWER8 processor.

IBM’s POWER group has partnered with NVIDIA to make GPUs available on a high-performance server platform, promising the next generation of parallel performance for Java applications. We decided to bring GPU support to Java incrementally using three approaches.

Enabling CUDA for Java Developers

Our first step brings capabilities of the CUDA programming model into the Java programming environment. Java developers familiar with CUDA concepts can use the new IBM CUDA4J library, which provides a Java API for managing and accessing GPU devices, libraries, kernels, and memory. Using these new APIs it is possible to write Java programs that manage GPU device characteristics and offload work to the GPU with the convenience of the Java memory model, exceptions, and automatic resource management that Java developers expect. Continue reading

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CUDA Pro Tip: Understand Fat Binaries and JIT Caching

As NVIDIA GPUs evolve to support new features, the instruction set architecture naturally changes. Because applications must run on multiple generations of GPUs, the NVIDIA compiler tool chain supports compiling for multiple architectures in the same application executable or library. CUDA also relies on the PTX virtual GPU ISA to provide forward compatibility, so that already deployed applications can run on future GPU architectures. In this post I will give you a basic understanding of CUDA “fat binaries” and compilation for multiple GPU architectures, as well as just-in-time PTX compilation for forward compatibility.

nvcc, the CUDA compiler driver, uses a two-stage compilation model. The first stage compiles source device code to PTX virtual assembly, and the second stage compiles the PTX to binary code for the target architecture. The CUDA driver can execute the second stage compilation at run time, compiling the PTX virtual assembly “Just In Time” to run it. This JIT compilation can cause delay at application start-up time (or more accurately, CUDA context creation time). CUDA uses two approaches to mitigate start-up overhead on JIT compilation: fat binaries and JIT caching.

Fat Binaries

The first approach is to completely avoid the JIT cost by including binary code for one or more architectures in the application binary along with PTX code. The CUDA run time looks for code for the present GPU architecture in the binary, and runs it if found. If binary code is not found but PTX is available, then the driver compiles the PTX code. In this way deployed CUDA applications can support new GPUs when they come out. Continue reading