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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Programming and Runtime Support to Blaze FPGA Accelerator Deployment at Datacenter Scale.

Muhuan Huang1, Di Wu1, Cody Hao Yu2

  • 1University of California Los Angeles; Falcon Computing Solutions, Inc.

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PubMed
Summary
This summary is machine-generated.

Blaze enables easy integration of FPGA accelerators into big data systems like Apache Spark and Hadoop. This approach significantly boosts throughput and energy efficiency for datacenter applications.

Keywords:
C.1.3 [Computer Systems Organization]FPGA-as-a-serviceHeterogeneous (hybrid) systemsheterogeneous datacenter

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Area of Science:

  • Computer Engineering
  • Data Science
  • Hardware Acceleration

Background:

  • CPU core scaling limitations necessitate alternative solutions for datacenter performance.
  • Field-Programmable Gate Arrays (FPGAs) offer a promising approach for low-power, high-performance computing.
  • Existing big data systems face challenges in leveraging FPGA accelerators.

Purpose of the Study:

  • To design and implement Blaze, a system providing programming and runtime support for FPGA accelerators in datacenters.
  • To enable easy and efficient deployment of FPGA accelerators within big data frameworks.
  • To address the challenges of integrating FPGAs into systems like Apache Spark and Hadoop.

Main Methods:

  • Abstracting FPGA accelerators as a service (FaaS) with clean programming APIs.
  • Implementing a Blaze runtime with an FaaS framework for efficient accelerator sharing on a single node.
  • Extending Hadoop YARN with accelerator-centric scheduling for cluster-wide resource management.

Main Results:

  • Blaze significantly reduces programming effort for utilizing FPGA accelerators.
  • Achieved system throughput improvements ranging from 1.7× to 3×.
  • Demonstrated energy efficiency gains of 1.5× to 2.7× compared to CPU-only clusters.

Conclusions:

  • Blaze effectively facilitates the adoption of FPGA accelerators in modern datacenters.
  • The system enhances both performance and energy efficiency for big data applications.
  • Blaze represents a key advancement for future datacenter growth and sustainable computing.