Generating Efficient Data Movement Code for Heterogeneous Architectures with Distributed-Memory

Roshan Dathathri, Chandan Reddy, Thejas Ramashekar, Uday Bondhugula
Department of Computer Science and Automation, Indian Institute of Science, Bangalore, India 560012
International conference on parallel architectures and compilation techniques (PACT 2013), 2013

   title={Generating Efficient Data Movement Code for Heterogeneous Architectures with Distributed-Memory},

   author={Dathathri, Roshan and Reddy, Chandan and Ramashekar, Thejas and Bondhugula, Uday},



Download Download (PDF)   View View   Source Source   



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 data communicated 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.
VN:F [1.9.22_1171]
Rating: 5.0/5 (2 votes cast)
Generating Efficient Data Movement Code for Heterogeneous Architectures with Distributed-Memory, 5.0 out of 5 based on 2 ratings

* * *

* * *

Follow us on Twitter

HGPU group

1666 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

339 people like HGPU on Facebook

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: nVidia CUDA Toolkit 6.5.14, AMD APP SDK 3.0
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.3
  • SDK: AMD APP SDK 3.0

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2015 hgpu.org

All rights belong to the respective authors

Contact us: