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FLAP: Tool to generate CUDA code from sequential C code

Erika Hernandez Rubio, Amilcar Meneses‐Viveros, Pedro M. Cortes Perez, Sergio D. Hernandez Zavala, Hector M. Martinez Rios

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Abstract

The GPU usage has increased in the high performance computing applications. However, still programming tools required to facilitate the development of programs for GPUs. It is well known that the loops in sequential programs are potential sources of parallelization. Three types of FOR-loops have been identified that can be mapped to CUDA kernel: single loops, nested loops and loops involving reduction operations. These loops can be mapped to CUDA kernels using patterns. In this work we present FLAP, a tool to generate CUDA parallel code from sequential C code. This tool uses these patterns to generate parallel code CUDA. We present some tests to show the performance of the code that generates FLAP.

About this research paper

What this paper is about

The GPU usage has increased in the high performance computing applications. However, still programming tools required to facilitate the development of programs for GPUs. It is well known that the loops in sequential programs are potential sources of parallelization. Three types of FOR-loops have been identified that can be mapped to CUDA kernel: single loops, nested loops and loops involving reduction operations. These loops can be mapped to CUDA kernels using patterns. In this work we present FLAP, a tool to generate CUDA parallel code from sequential C code. This tool uses these patterns to generate parallel code CUDA. We present some tests to show the performance of the code that generates FLAP.

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Available abstract

The GPU usage has increased in the high performance computing applications. However, still programming tools required to facilitate the development of programs for GPUs. It is well known that the loops in sequential programs are potential sources of parallelization. Three types of FOR-loops have been identified that can be mapped to CUDA kernel: single loops, nested loops and loops involving reduction operations. These loops can be mapped to CUDA kernels using patterns. In this work we present FLAP, a tool to generate CUDA parallel code from sequential C code. This tool uses these patterns to generate parallel code CUDA. We present some tests to show the performance of the code that generates FLAP.

Key concepts: CUDA, Computer science, Parallel computing, Code (set theory), Kernel (algebra), General-purpose computing on graphics processing units, Source code, Programming language

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