Optimizing GPU Kernels for Irregular Batch Workloads: A Case Study for Cholesky Factorization

Ahmad Abdelfattah; Azzam Haidar; Stanimire Tomov; Jack Dongarra

doi:10.1109/HPEC.2018.8547576

Optimizing GPU Kernels for Irregular Batch Workloads: A Case Study for Cholesky Factorization

Ahmad Abdelfattah, Azzam Haidar, Stanimire Tomov, Jack Dongarra

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

This paper introduces several frameworks for the design and implementation of high performance GPU kernels that target batch workloads with irregular sizes. Such workloads are ubiquitous in many scientific applications, including sparse direct solvers, astrophysics, and quantum chemistry. The paper addresses two main categories of frameworks, taking the Cholesky factorization as a case study. The first uses host-side kernel launches, and the second uses device-side launches. Within each category, different design options are introduced, with an emphasis on the advantages and the disadvantages of each approach. Our best performing design outperforms the state-of-the-art CPU implementation, scoring up to 4.7× speedup in double precision on a Pascal P100 GPU.

Original language	English
Title of host publication	2018 IEEE High Performance Extreme Computing Conference, HPEC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538659892
DOIs	https://doi.org/10.1109/HPEC.2018.8547576
State	Published - Nov 26 2018
Externally published	Yes
Event	2018 IEEE High Performance Extreme Computing Conference, HPEC 2018 - Waltham, United States Duration: Sep 25 2018 → Sep 27 2018

Publication series

Name	2018 IEEE High Performance Extreme Computing Conference, HPEC 2018

Conference

Conference	2018 IEEE High Performance Extreme Computing Conference, HPEC 2018
Country/Territory	United States
City	Waltham
Period	09/25/18 → 09/27/18

Funding

This work is partially supported by NSF grant No. OAC-1740250 and CSR 1514286, NVIDIA, and by the Exascale Computing Project (17-SC-20-SC). This work is partially supported by NSF grant No. OAC-1740250 and CSR 1514286, NVIDIA, and by the Exascale Computing Project (17-SC-20-SC)

Funders	Funder number
National Science Foundation
Center for Scientific Review	1514286
Norsk Sykepleierforbund	OAC-1740250

Keywords

Batch Linear Algebra
GPU Computing
Matrix Factorization

Access to Document

10.1109/HPEC.2018.8547576

Cite this

Abdelfattah, A., Haidar, A., Tomov, S., & Dongarra, J. (2018). Optimizing GPU Kernels for Irregular Batch Workloads: A Case Study for Cholesky Factorization. In 2018 IEEE High Performance Extreme Computing Conference, HPEC 2018 Article 8547576 (2018 IEEE High Performance Extreme Computing Conference, HPEC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/HPEC.2018.8547576

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title = "Optimizing GPU Kernels for Irregular Batch Workloads: A Case Study for Cholesky Factorization",

abstract = "This paper introduces several frameworks for the design and implementation of high performance GPU kernels that target batch workloads with irregular sizes. Such workloads are ubiquitous in many scientific applications, including sparse direct solvers, astrophysics, and quantum chemistry. The paper addresses two main categories of frameworks, taking the Cholesky factorization as a case study. The first uses host-side kernel launches, and the second uses device-side launches. Within each category, different design options are introduced, with an emphasis on the advantages and the disadvantages of each approach. Our best performing design outperforms the state-of-the-art CPU implementation, scoring up to 4.7× speedup in double precision on a Pascal P100 GPU.",

keywords = "Batch Linear Algebra, GPU Computing, Matrix Factorization",

author = "Ahmad Abdelfattah and Azzam Haidar and Stanimire Tomov and Jack Dongarra",

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year = "2018",

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doi = "10.1109/HPEC.2018.8547576",

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Abdelfattah, A, Haidar, A, Tomov, S & Dongarra, J 2018, Optimizing GPU Kernels for Irregular Batch Workloads: A Case Study for Cholesky Factorization. in 2018 IEEE High Performance Extreme Computing Conference, HPEC 2018., 8547576, 2018 IEEE High Performance Extreme Computing Conference, HPEC 2018, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE High Performance Extreme Computing Conference, HPEC 2018, Waltham, United States, 09/25/18. https://doi.org/10.1109/HPEC.2018.8547576

Optimizing GPU Kernels for Irregular Batch Workloads: A Case Study for Cholesky Factorization. / Abdelfattah, Ahmad; Haidar, Azzam; Tomov, Stanimire et al.
2018 IEEE High Performance Extreme Computing Conference, HPEC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8547576 (2018 IEEE High Performance Extreme Computing Conference, HPEC 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - This paper introduces several frameworks for the design and implementation of high performance GPU kernels that target batch workloads with irregular sizes. Such workloads are ubiquitous in many scientific applications, including sparse direct solvers, astrophysics, and quantum chemistry. The paper addresses two main categories of frameworks, taking the Cholesky factorization as a case study. The first uses host-side kernel launches, and the second uses device-side launches. Within each category, different design options are introduced, with an emphasis on the advantages and the disadvantages of each approach. Our best performing design outperforms the state-of-the-art CPU implementation, scoring up to 4.7× speedup in double precision on a Pascal P100 GPU.

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Abdelfattah A, Haidar A, Tomov S, Dongarra J. Optimizing GPU Kernels for Irregular Batch Workloads: A Case Study for Cholesky Factorization. In 2018 IEEE High Performance Extreme Computing Conference, HPEC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 8547576. (2018 IEEE High Performance Extreme Computing Conference, HPEC 2018). doi: 10.1109/HPEC.2018.8547576

Optimizing GPU Kernels for Irregular Batch Workloads: A Case Study for Cholesky Factorization

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