DevMesh

Boosting epistasis detection on Intel CPU+GPU systems

Rafael Campos

Rafael Campos

Lisbon, Lisbon

6 0

This work focuses on exploring the architecture of Intel CPUs and Integrated Graphics and their heterogeneous computing potential to boost performance and energy-efficiency of epistasis detection. This will be achieved making use of OpenCL, Data Parallel C++ and OpenMP programming models. ...learn more

Project status: Published/In Market

oneAPI, HPC

Intel Technologies
oneAPI, DPC++, Intel Integrated Graphics, Intel vTune, Intel CPU, DevCloud

Docs/PDFs [1]Code Samples [1]Links [3]

Overview / Usage

Epistasis Detection is a fundamental part of genome-wide association studies in modern bioinformatics. Exhaustive search of epistasis detection provides the most reliable way to identify accurate solutions, but it is both computationally and memory intensive task. This work proposes the use of heterogeneous computer architectures composed of multi-core CPUs and integrated GPUs to achieve high-performance and energy-efficient epistasis analysis.

Methodology / Approach

A set of traditional programming models, i.e., OpenCL and OpenMP, and novel standards like Data-Parallel C++ are used to boost the performance, power and energy-efficiency of bioinformatic applications. oneAPI tools such as Intel Advisor and Intel VTune Profiler are used to aid the development, by providing metrics on performance and improvement guides. Techniques used include parallelization using multi-core CPUs and integrated GPU, cache blocking and vectorization.

Technologies Used

8th Generation Intel Core CPU, Intel Integrated Graphics Gen9.5, OpenCL 2.0, OpenMP, Data-Parallel C++, oneAPI base toolkit, Intel Advisor, Intel vTune Profiler

Documents and Presentations

oneAPI_DevCon20_Rooflining_Epistasis.pdf

Repository

https://github.com/hiperbio/cross-dpc-episdet

Collaborators

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