NDLI: Generating efficient data movement code for heterogeneous architectures with distributed-memory

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Generating efficient data movement code for heterogeneous architectures with distributed-memory

Content Provider	ACM Digital Library
Author	Dathathri, Roshan Ramashekar, Thejas Bondhugula, Uday Reddy, Chandan
Abstract	Programming for parallel architectures that do not have a shared address space is extremely difficult due to the need for explicit communication between memories of different compute devices. A heterogeneous system with CPUs and multiple GPUs, or a distributed-memory cluster are examples of such systems. Past works that try to automate data movement for distributed-memory architectures can lead to excessive redundant communication. In this paper, we propose an automatic data movement scheme that minimizes the volume of communication between compute devices in heterogeneous and distributed-memory systems. We show that by partitioning data dependences in a particular non-trivial way, one can generate data movement code that results in the minimum volume for a vast majority of cases. The techniques are applicable to any sequence of affine loop nests and works on top of any choice of loop transformations, parallelization, and computation placement. The data movement code generated minimizes the volume of communication for a particular configuration of these. We use a combination of powerful static analyses relying on the polyhedral compiler framework and lightweight runtime routines they generate, to build a source-to-source transformation tool that automatically generates communication code. We demonstrate that the tool is scalable and leads to substantial gains in efficiency. On a heterogeneous system, the communication volume is reduced by a factor of 11x to 83x over state-of-the-art, translating into a mean execution time speedup of 1.53x. On a distributed-memory cluster, our scheme reduces the communication volume by a factor of 1.4x to 63.5x over state-of-the-art, resulting in a mean speedup of 1.55x. In addition, our scheme yields a mean speedup of 2.19x over hand-optimized UPC codes.
Starting Page	375
Ending Page	386
Page Count	12
File Format	PDF
ISBN	9781479910212
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-07
Access Restriction	Subscribed
Subject Keyword	Distributed memory Polyhedral model Communication optimization Data movement Heterogeneous architectures
Content Type	Text
Resource Type	Article

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