Heterogeneous CPU+iGPU Processing for Efficient Epistasis Detection

Campos, Rafael Saraiva; Marques, Diogo; Santander-Jiménez, Sergio; Sousa, Leonel; Ilić, Aleksandar

doi:10.1007/978-3-030-57675-2_38

Cited by 6 publications

(12 citation statements)

References 16 publications

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“…For the Intel Gen9.5 GPU, the proposed approach attains a performance around 62.3 Giga combs. x samples / s, i.e., 10.6x higher than the performance obtained in [30], while for Intel 8700K, the performance of the proposed method is 10.4x higher than the one obtained in [30].…”

Section: E Comparison With State-of-the-artmentioning

confidence: 59%

“…The work in [30] uses CPU and GPU in a collaborative way, performing two-way and three-way epistasis detection using CPU and GPU. However, the proposed framework focuses on the architectures of Intel CPUs and integrated GPUs.…”

Section: Related Workmentioning

confidence: 99%

“…The performance of the best approach for three-way epistasis detection proposed in this work is also compared against state-of-the-art works on three-way epistasis, namely MPI3SNP [27], and the works presented in [29] and [30]. The MPI3SNP results were obtained by executing the application on the experimental platforms used in this work, with the data sets contained in the project repository 2 .…”

Section: E Comparison With State-of-the-artmentioning

confidence: 99%

“…Similarly, the results for [29] were obtained through experimental evaluation on the considered devices. The values from [30] are directly obtained from the respective manuscript.…”

Section: E Comparison With State-of-the-artmentioning

confidence: 99%

See 3 more Smart Citations

Unlocking Personalized Healthcare on Modern CPUs/GPUs: Three-way Gene Interaction Study

Marques¹,

Campos²,

Santander-Jiménez³

et al. 2022

Preprint

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Developments in Genome-Wide Association Studies have led to the increasing notion that future healthcare techniques will be personalized to the patient, by relying on genetic tests to determine the risk of developing a disease. To this end, the detection of gene interactions that cause complex diseases constitutes an important application. Similarly to many applications in this field, extensive data sets containing genetic information for a series of patients are used (such as Single-Nucleotide Polymorphisms), leading to high computational complexity and memory utilization, thus constituting a major challenge when targeting high-performance execution in modern computing systems. To close this gap, this work proposes several novel approaches for the detection of three-way gene interactions in modern CPUs and GPUs, making use of different optimizations to fully exploit the target architectures. Crucial insights from the Cache-Aware Roofline Model are used to ensure the suitability of the applications to the computing devices. An extensive study of the architectural features of 13 CPU and GPU devices from all main vendors is also presented, allowing to understand the features relevant to obtain high-performance in this bioinformatics domain. To the best of our knowledge, this study is the first to perform such evaluation for epistasis detection. The proposed approaches are able to surpass the performance of state-of-the-art works in the tested platforms, achieving an average speedup of 3.9× (7.3× on CPUs and 2.8× on GPUs) and maximum speedup of 10.6× on Intel UHD P630 GPU.

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Section: E Comparison With State-of-the-artmentioning

confidence: 59%

Section: Related Workmentioning

confidence: 99%

Section: E Comparison With State-of-the-artmentioning

confidence: 99%

“…Similarly, the results for [29] were obtained through experimental evaluation on the considered devices. The values from [30] are directly obtained from the respective manuscript.…”

Section: E Comparison With State-of-the-artmentioning

confidence: 99%

See 2 more Smart Citations

Unlocking Personalized Healthcare on Modern CPUs/GPUs: Three-way Gene Interaction Study

Marques¹,

Campos²,

Santander-Jiménez³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…BitEpi implements a 2, 3 and 4-locus epistasis search that also uses multithreading to speed up the search. Furthermore, for the x86_64 CPU architecture, there are some publications that discuss AVX vector implementations of the epistasis search [6,7].…”

Section: Related Workmentioning

confidence: 99%

Fiuncho: a program for any-order epistasis detection in CPU clusters

Ponte-Fernández¹,

González-Domínguez²,

Martín³

2022

Preprint

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Epistasis can be defined as the statistical interaction of genes during the expression of a phenotype. It is believed that it plays a fundamental role in gene expression, as individual genetic variants have reported a very small increase in disease risk in previous Genome-Wide Association Studies. The most successful approach to epistasis detection is the exhaustive method, although its exponential time complexity requires a highly parallel implementation in order to be used. This work presents Fiuncho, a program that exploits all levels of parallelism present in x86_64 CPU clusters in order to mitigate the complexity of this approach. It supports epistasis interactions of any order, and when compared with other exhaustive methods, it is on average 242, 7 and 3 times faster than MDR, BitEpi and MPI3SNP, respectively.

show abstract

Fiuncho: a program for any-order epistasis detection in CPU clusters

2022

View full text Add to dashboard Cite

Epistasis can be defined as the statistical interaction of genes during the expression of a phenotype. It is believed that it plays a fundamental role in gene expression, as individual genetic variants have reported a very small increase in disease risk in previous Genome-Wide Association Studies. The most successful approach to epistasis detection is the exhaustive method, although its exponential time complexity requires a highly parallel implementation in order to be used. This work presents Fiuncho, a program that exploits all levels of parallelism present in x86_64 CPU clusters in order to mitigate the complexity of this approach. It supports epistasis interactions of any order, and when compared with other exhaustive methods, it is on average 358, 7 and 3 times faster than MDR, MPI3SNP and BitEpi, respectively.

show abstract

Heterogeneous CPU+iGPU Processing for Efficient Epistasis Detection

Cited by 6 publications

References 16 publications

Unlocking Personalized Healthcare on Modern CPUs/GPUs: Three-way Gene Interaction Study

Unlocking Personalized Healthcare on Modern CPUs/GPUs: Three-way Gene Interaction Study

Fiuncho: a program for any-order epistasis detection in CPU clusters

Fiuncho: a program for any-order epistasis detection in CPU clusters

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