NDLI: Scheduling Techniques for GPU Architectures with Processing-In-Memory Capabilities

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Scheduling Techniques for GPU Architectures with Processing-In-Memory Capabilities

Content Provider	ACM Digital Library
Author	Mishra, Asit K. Kayiran, Onur Tang, Xulong Jog, Adwait Das, Chita R. Kandemir, Mahmut T. Mutlu, Onur Pattnaik, Ashutosh
Abstract	Processing data in or near memory (PIM), as opposed to in conventional computational units in a processor, can greatly alleviate the performance and energy penalties of data transfers from/to main memory. Graphics Processing Unit (GPU) architectures and applications, where main memory bandwidth is a critical bottleneck, can benefit from the use of PIM. To this end, an application should be properly partitioned and scheduled to execute on either the main, powerful GPU cores that are far away from memory or the auxiliary, simple GPU cores that are close to memory (e.g., in the logic layer of 3D-stacked DRAM). This paper investigates two key code scheduling issues in such a GPU architecture that has PIM capabilities, to maximize performance and energy-efficiency: (1) how to automatically identify the code segments, or kernels, to be offloaded to the cores in memory, and (2) how to concurrently schedule multiple kernels on the main GPU cores and the auxiliary GPU cores in memory. We develop two new runtime techniques: (1) a regression-based affinity prediction model and mechanism that accurately identifies which kernels would benefit from PIM and offloads them to GPU cores in memory, and (2) a concurrent kernel management mechanism that uses the affinity prediction model, a new kernel execution time prediction model, and kernel dependency information to decide which kernels to schedule concurrently on main GPU cores and the GPU cores in memory. Our experimental evaluations across 25 GPU applications demonstrate that these two techniques can significantly improve both application performance (by 25% and 42%, respectively, on average) and energy efficiency (by 28% and 27%).
Starting Page	31
Ending Page	44
Page Count	14
File Format	PDF
ISBN	9781450341219
DOI	10.1145/2967938.2967940
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-09-11
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Near data computing Gpu Kernel scheduling Processing-in-memory
Content Type	Text
Resource Type	Article

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