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Runtime and Architecture Support for Efficient Data Exchange in Multi-Accelerator Applications.

Javier Cabezas1, Isaac Gelado2, John E Stone3

  • 1Department of Computer Science, Barcelona Supercomputing Center and with the Universitat Politècnica de Catalunya, Barcelona, Spain.

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Summary
This summary is machine-generated.

The HPE runtime system simplifies data exchange between multiple GPUs, significantly reducing programming complexity and boosting performance for heterogeneous computing applications. This innovation enhances efficiency and maintainability in parallel processing.

Keywords:
Data communicationsDistributed architecturesHardware/software interfacesHeterogeneous (hybrid) systems

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Area of Science:

  • * Parallel Computing
  • * Computer Architecture
  • * Software Engineering

Background:

  • * Heterogeneous parallel applications demand substantial memory and compute resources, often requiring multiple GPUs.
  • * Existing programming models for multi-GPU systems lack high-level abstractions, leading to complex code, data management, and synchronization.
  • * This complexity increases development costs, reduces maintainability, and can hinder performance.

Purpose of the Study:

  • * To introduce the HPE runtime system and its architectural support for simplified multi-GPU data exchange.
  • * To demonstrate how this system reduces programming complexity and improves efficiency in heterogeneous computing.
  • * To evaluate the effectiveness of hardware support for HPE features on real-world benchmarks.

Main Methods:

  • * Development and implementation of the HPE runtime system with associated architecture support.
  • * Integration of HPE features into NVIDIA GPU hardware and CUDA drivers.
  • * Performance evaluation using benchmarks on real heterogeneous systems with HPE-enabled hardware and runtime.

Main Results:

  • * The HPE system offers a simple programming interface for inter-GPU data exchange via interconnects or network interfaces.
  • * Experimental results show a 2x improvement in inter-accelerator data communication bandwidth.
  • * Significant speedups were observed: 1.6x for 3D finite difference, 2.5x for 1D FFT, and 1.6x for merge sort.

Conclusions:

  • * The HPE runtime system and architecture support effectively simplify programming for multi-GPU systems.
  • * Hardware support for HPE features enables transparent optimization, improving performance for complex applications.
  • * Simple, high-level interfaces like HPE are crucial for practical utilization of advanced hardware features in heterogeneous computing.