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Compiler-directive based Heterogeneous Computing for Scala

Jungjae Woo, Seongsoo Park, Sungin Hong, Hwansoo Han

http://doi.org/10.5626/JOK.2023.50.3.197

With the advent of the big data era, heterogeneous computing is employed to process large amounts of data. Since Apache Spark, a representative big data analysis framework, is built with the Scala programming language, programs written in Scala need to be rewritten with CUDA, OpenCL, and others to enjoy the benefits of GPU computing. TornadoVM automatically converts Java programs into OpenCL programs using compiler annotations defined in the Java specification. Scala shares bytecode in an executable form with Java, but the annotation capabilities of current Scala compilers lack the annotations indispensable for TornadoVM’s OpenCL translation. In this work, the annotation capabilities of Scala compilers are extended to enable OpenCL translation on TornadoVM. Furthermore, we experimentally confirmed that the performance of Scala-OpenCL converted code is as fast as Java-OpenCL converted code. With our extension, we expect Scala programs to easily use GPU acceleration in the Apache Spark framework.

PARPA: A Parallel Framework Simultaneously Using Heterogeneous Architecture for High Performance Computing

Hyojae Cho, Taehyun Han, Hyeonmyeong Lee, Heeseung Jo

http://doi.org/10.5626/JOK.2019.46.9.876

With the substantial performance improvements achieved in GPU, they have come to be commonly used not only in computer graphics but also in high performance computing. Simply using a CPU and a GPU concurrently is not difficult. However, distributing works and adjusting the computing ratio among these heterogeneous processors are challenging issues. We propose a novel framework in this paper, named PARPA, which automatically distributes and processes tasks to a CPU and a GPU. PARPA can maximize computation performance by using a CPU and a GPU simultaneously. The load balancing between them can be performed dynamically based on their usage and features. The evaluation results indicate that PARPA shows 3.48 times better performance.


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