Fancier: A Unified <underline>F</underline>ramework for J<underline>a</underline>va, C, a<underline>n</underline>d Open<underline>C</underline>L <underline>I</underline>nt<underline>e</underline>g<underline>r</underline>ation

Abstract

Graphics Processing Units (GPUs) have evolved from very specialized designs geared towards computer graphics to accommodate general-purpose highly-parallel workloads. Harnessing the performance that these accelerators provide requires the use of specialized native programming interfaces, such as CUDA or OpenCL, or higher-level programming models like OpenMP or OpenACC. However, on managed programming languages, offloading execution into GPUs is much harder and error-prone, mainly due to the need to call through a native API (Application Programming Interface), and because of mismatches between value and reference semantics. The Fancier framework provides a unified interface to Java, C/C++, and OpenCL C compute kernels, together with facilities to smooth the transitions between these programming languages. This combination of features makes GPU acceleration on Java much more approachable. In addition, Fancier Java code can be directly translated into equivalent C/C++ or OpenCL C code easily, which simplifies the implementation of higher-level abstractions targeting GPU or parallel execution on Java. Furthermore, it reduces the programming effort without adding significant overhead on top of the necessary OpenCL and Java Native Interface (JNI) API calls. We validate our approach on several image processing workloads running on different Android devices.

Keywords

• Application programming interfaces
• hardware acceleration
• heterogeneous systems
• image processing
• mobile computing
• parallel programming