NDLI: Improving performance by matching imbalanced workloads with heterogeneous platforms

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Improving performance by matching imbalanced workloads with heterogeneous platforms

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Improving performance by matching imbalanced workloads with heterogeneous platforms

Content Provider	ACM Digital Library
Author	Shen, Jie Sips, Henk Varbanescu, Ana Lucia Lu, Yutong Zou, Peng
Abstract	Although GPUs are considered ideal to accelerate massively data-parallel applications, there are still exceptions to this rule. For example, imbalanced applications cannot be efficiently processed by GPUs: despite the massive data parallelism, a varied computational workload per data point remains GPU-unfriendly. To efficiently process imbalanced applications, we exploit the use of heterogeneous platforms (GPUs and CPUs) by partitioning the workload to fit the usage patterns of the processors. In this work, we present our flexible and adaptive method that predicts the optimal partitioning. Our method aims to match a quantitative model of the application with the hardware capabilities of the platform, and calculates the optimal match according to a user-given criterion. We evaluate our method in terms of overall performance gain, prediction accuracy, flexibility and adaptivity. Our results, gathered from both synthetic and real-world workloads, show performance gains of up to 60%, accurate predictions for more than 90% of all the 1395 imbalanced workloads we have tested, and confirm that the method adapts correctly to application, dataset, and platform changes (both hardware and software). We conclude that model-based prediction of workload partitioning for heterogeneous platforms is feasible and useful for performance improvement.
Starting Page	241
Ending Page	250
Page Count	10
File Format	PDF
ISBN	9781450326421
DOI	10.1145/2597652.2597675
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-06-10
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Workload partitioning Imbalanced workloads Gpus Multi-core cpus Heterogeneous platforms
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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