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Open Access Publications from the University of California

Scholarly Works (2 results)

  • Thesis
  • Peer Reviewed
UCLA Electronic Theses and Dissertations (2016)

In many domains, accelerators---such as graphic processing units (GPUs) and field programmable gate arrays (FPGAs)---provide a significantly higher performance than general-purpose processors and at a much lower power. Accelerator-rich architectures are thus much more energy-efficient and are becoming mainstream.

This dissertation investigates two important keys to the performance and power efficiency of accelerator-rich architectures---resource and data management. Three broad classes of accelerator-rich architectures are considered: chip-level accelerator-rich architectures such as systems-on-chips(SoC), node-level accelerator-rich architectures, and cluster-level accelerator-rich architectures.

We first study SoC resource management for a broader class of streaming applications. On accelerator-rich SoCs, where multiple computation kernels space-share a single chip, we target the exploration of tradeoffs of on-chip resources and system performance, and find the best combination of accelerator implementations and data communication channel implementations to realize the application functionality.

We continue our study of node-level accelerator-rich architectures where we consider orchestrating two kinds of computation resources, CPU and accelerator, in the PCIe-integrated CPU-accelerator platform and explore the CPU-FPGA collaboration approach to improve application performance.

Then we study the resource allocation problem on accelerator-rich clusters, where accelerators are time-shared among multiple tenants. Unlike traditional cluster resource management, we propose to consider accelerators as the first-class citizen in the cluster resource pool, and develop an accelerator-centric resource scheduling policy to enable fine-grained accelerator sharing among multiple tenants.

Finally, we investigate data shuffling on accelerator-rich clusters and evaluate the possibility of using accelerators during data shuffling. We find that although data shuffling involves a large amount of computation, using accelerators does not necessarily improve system performance due to the data serialization and deserialization overhead introduced by accelerators.

    Cover page: Resource and Data Management in Accelerator-Rich Architectures
    • Article
    • Peer Reviewed
    UCLA Previously Published Works (2016)
    With the end of CPU core scaling due to dark silicon limitations, customized accelerators on FPGAs have gained increased attention in modern datacenters due to their lower power, high performance and energy efficiency. Evidenced by Microsoft's FPGA deployment in its Bing search engine and Intel's 16.7 billion acquisition of Altera, integrating FPGAs into datacenters is considered one of the most promising approaches to sustain future datacenter growth. However, it is quite challenging for existing big data computing systems-like Apache Spark and Hadoop-to access the performance and energy benefits of FPGA accelerators. In this paper we design and implement Blaze to provide programming and runtime support for enabling easy and efficient deployments of FPGA accelerators in datacenters. In particular, Blaze abstracts FPGA accelerators as a service (FaaS) and provides a set of clean programming APIs for big data processing applications to easily utilize those accelerators. Our Blaze runtime implements an FaaS framework to efficiently share FPGA accelerators among multiple heterogeneous threads on a single node, and extends Hadoop YARN with accelerator-centric scheduling to efficiently share them among multiple computing tasks in the cluster. Experimental results using four representative big data applications demonstrate that Blaze greatly reduces the programming efforts to access FPGA accelerators in systems like Apache Spark and YARN, and improves the system throughput by 1.7 × to 3× (and energy efficiency by 1.5× to 2.7×) compared to a conventional CPU-only cluster.
      Cover page: Programming and Runtime Support to Blaze FPGA Accelerator Deployment at Datacenter Scale
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