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The Journal of Supercomputing
Article . 2024 . Peer-reviewed
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Assessing opportunities of SYCL for biological sequence alignment on GPU-based systems

تقييم فرص SYCL لمحاذاة التسلسل البيولوجي على الأنظمة القائمة على GPU
Authors: Manuel Costanzo; Enzo Rucci; Carlos García; Marcelo Naiouf; Manuel Prieto;

Assessing opportunities of SYCL for biological sequence alignment on GPU-based systems

Abstract

AbstractBioinformatics and computational biology are two fields that have been exploiting GPUs for more than two decades, with being CUDA the most used programming language for them. However, as CUDA is an NVIDIA proprietary language, it implies a strong portability restriction to a wide range of heterogeneous architectures, like AMD or Intel GPUs. To face this issue, the Khronos group has recently proposed the SYCL standard, which is an open, royalty-free, cross-platform abstraction layer that enables the programming of a heterogeneous system to be written using standard, single-source C++ code. Over the past few years, several implementations of this SYCL standard have emerged, being oneAPI the one from Intel. This paper presents the migration process of theSW# suite, a biological sequence alignment tool developed in CUDA, to SYCL using Intel’s oneAPI ecosystem. The experimental results show thatSW# was completely migrated with a small programmer intervention in terms of hand-coding. In addition, it was possible to port the migrated code between different architectures (considering multiple vendor GPUs and also CPUs), with no noticeable performance degradation on five different NVIDIA GPUs. Moreover, performance remained stable when switching to another SYCL implementation. As a consequence, SYCL and its implementations can offer attractive opportunities for the bioinformatics community, especially considering the vast existence of CUDA-based legacy codes.

Keywords

Parallel computing, Programmer, FOS: Computer and information sciences, Bioinformatics, GPU, CUDA, Sequence alignment, Artificial Intelligence, Biochemistry, Genetics and Molecular Biology, 1203.17 Informática, RNA Sequencing Data Analysis, Molecular Biology, SYCL, Computer Science - Programming Languages, Prediction of Protein Subcellular Localization, oneAPI, Protein, Life Sciences, DNA, Computer science, Programming language, Computer Science - Distributed, Parallel, and Cluster Computing, Ciencias, Application of Genetic Programming in Machine Learning, Implementation, SYCLomatic, Computer Science, Physical Sciences, sequence alignment, Distributed, Parallel, and Cluster Computing (cs.DC), Software portability, Programming Languages (cs.PL)

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    popularity
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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Top 10%
Average
Average
Green
hybrid