Field-Programmable Gate Arrays (FPGAs) are more energy efficient and cost effective than CPUs for a wide variety of datacenter applications. Yet, for latency-sensitive and bursty workloads, this advantage can be difficult to harness due to high FPGA spin-up costs. We propose that a hybrid FPGA and CPU computing framework can harness the energy efficiency benefits of FPGAs for such workloads at reasonable cost. Our key insight is to use FPGAs for stable-state workload and CPUs for short-term workload bursts. Using this insight, we design Spork, a lightweight hybrid scheduler that can realize these energy efficiency and cost benefits in practice. Depending on the desired objective, Spork can trade off energy efficiency for cost reduction and vice versa. It is parameterized with key differences between FPGAs and CPUs in terms of power draw, performance, cost, and spin-up latency. We vary this parameter space and analyze various application and worker configurations on production and synthetic traces. Our evaluation of cloud workloads shows that energy-optimized Spork is not only more energy efficient but it is also cheaper than homogeneous platforms--for short application requests with tight deadlines, it is 1.53x more energy efficient and 2.14x cheaper than using only FPGAs. Relative to an idealized version of an existing cost-optimized hybrid scheduler, energy-optimized Spork provides 1.2-2.4x higher energy efficiency at comparable cost, while cost-optimized Spork provides 1.1-2x higher energy efficiency at 1.06-1.2x lower cost.
翻译:Field-Programmable Gate Arrays(FPGAs)比CPU在许多数据中心应用中更具有能效和成本效益。然而,对于对延迟敏感和突发的工作负载,由于高昂的FPGA初始成本,难以利用这种优势。我们提出了一种混合FPGA和CPU计算框架,可以在合理的成本下利用FPGA的能效优势。我们的关键洞察力是将FPGA用于稳定状态的工作负载,将CPU用于短期工作负载突发。基于这一洞察力,我们设计了Spork,一种轻量级混合调度器,可以在实践中实现这些能效和成本效益。根据期望的目标,Spork可以权衡能效和成本降低之间的权衡。它与FPGA和CPU在功率消耗,性能,成本和旋转延迟等关键差异有关的参数化。我们改变这个参数空间,并在生产和合成跟踪上分析各种应用程序和工人配置。我们评估云工作负载,发现优化能源的Spork不仅更加能效,而且还比同构平台便宜-对于短应用请求和紧急期限,它比仅使用FPGA更省电1.53倍,成本更低2.14倍。相对于现有成本优化的混合调度器的理想化版本,优化能源的Spork提供1.2-2.4倍的更高能效,成本相当,而优化成本的Spork在1.06-1.2倍较低的成本下提供1.1-2倍的更高能效。