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Open Access Publications from the University of California

Scholarly Works (2 results)

  • Thesis
  • Peer Reviewed
UC San Diego Electronic Theses and Dissertations (2024)

The dissertation investigates redundant communication between servers for large-scaleweb and cache requests and redundant data movement between accelerators for compute-intensive applications. Redundancy is an impending and critical issue for data centers designed for hardware accelerators and disaggregated resources. The dissertation makes the following three contributions to address this. The first contribution of the dissertation is Daronpon. Daronpon dynamically load-balances and reroutes large-scale requests of web and cache applications on a microsecond timescale. Daronpon prevents these requests, stranded on busy servers with network congestion and long queuing delays, from being processed. Daronpon shows improvement in various service time characterizations of different applications. The second contribution of the dissertation is Fianchetto. Fianchetto acts as a compute-enabled bypass for inter-accelerator communication. Fianchetto accelerates the data restructuring needed between accelerators and saves the data movement between accelerators and CPUs for compute-intensive applications. Fianchetto shows improvement in a series of benchmarks involving different application domains. The third contribution of the dissertation is Aurelia. Aurelia leverages the emerging interconnect of CXL to investigate the design of a scalable fabric for accelerators and fabric-attached memory expansion. Aurelia improves routing and transport based on the current specification of CXL and shows performance improvement on machine learning and key-value store applications

    Cover page: Accelerating Data Movement at Different Granularities in Datacenters
    • Article
    • Peer Reviewed
    UC San Diego Previously Published Works (2022)
    FPGA accelerators offer performance and efficiency gains by narrowing the scope of acceleration to one algorithmic domain. However, real-life applications are often not limited to a single domain, which naturally makes Cross-Domain Multi-Acceleration a crucial next step. The challenge is, existing FPGA accelerators are built upon their specific vertically-specialized stacks, which prevents utilizing multiple accelerators from different domains. To that end, we propose a pair of dual abstractions, called Yin-Yang, which work in tandem and enable programmers to develop cross-domain applications using multiple accelerators on a FPGA. The Yin abstraction enables cross-domain algorithmic specification, while the Yang abstraction captures the accelerator capabilities. We also develop a dataflow virtual machine, dubbed XLVM, that transparently maps domain functions (Yin) to best-fit accelerator capabilities (Yang). With six real-world cross-domain applications, our evaluations show that Yin-Yang unlocks 29.4× speedup, while the best single-domain acceleration achieves 12.0×.
      Cover page: Yin-Yang: Programming Abstractions for Cross-Domain Multi-Acceleration.
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