MOCHA: Multinode Cost Optimization in Heterogeneous Clouds with Accelerators
Channel: UCLA VAST
Date: 18/03/2021

Presented by Dr. Peipei Zhou at FPGA 2021.

FPGAs have been widely deployed in public clouds, e.g., Amazon Web Services (AWS) and Huawei Cloud. However, simply offloading accelerated kernels from CPU hosts to PCIe-based FPGAs does not guarantee out-of-pocket cost savings in a pay-as-you-go public cloud. Taking Genome Analysis Toolkit (GATK) applications as case studies, although the adoption of FPGAs reduces the overall execution time, it introduces 2.56× extra cost, due to insufficient application-level speedup by Amdahl's law. To optimize the out-of-pocket cost while keeping high speedup and throughput, we propose Mocha framework as a distributed runtime system to fully utilize the accelerator resource by accelerator sharing and CPU-FPGA partial task offloading. Evaluation results on Haplotype Caller (HTC) and Mutect2 in GATK show that on AWS, Mocha saves on the application cost by 2.82x for HTC, 1.06x for Mutect2 and on Huawei Cloud by 1.22x, 1.52x respectively than straightforward CPU-FPGA integration solution with less than 5.1% performance overhead.

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The VAST lab at UCLA investigates cutting-edge research topics at the intersection of VLSI technologies, design automation,  architecture, and compiler optimization at multiple scales, from micro-architecture building blocks to heterogeneous compute nodes and scalable data centers.  Current focuses include architecture and design automation for emerging technologies, customizable domain-specific computing with applications to multiple domains, such as imaging processing, bioinformatics, data mining, and machine learning.