Title

BifurKTM: Approximately consistent distributed transactional memory for GPUs

Document Type

Conference Proceeding

Publication Date

3-1-2021

Abstract

We present BifurKTM, the first read-optimized Distributed Transactional Memory system for GPU clusters. The BifurKTM design includes: GPU KoSTM, a new software transactional memory conflict detection scheme that exploits relaxed consistency to increase throughput; and KoDTM, a Distributed Transactional Memory model that combines the Data- and Control- flow models to greatly reduce communication overheads. Despite the allure of huge speedups, GPUs are limited in use due to their programmability and extreme sensitivity to workload characteristics. These become daunting concerns when considering a distributed GPU cluster, wherein a programmer must design algorithms to hide communication latency by exploiting data regularity, high compute intensity, etc. The BifurKTM design allows GPU programmers to exploit a new workload characteristic: the percentage of the workload that is Read-Only (e.g. reads but does not modify shared memory), even when this percentage is not known in advance. Programmers designate transactions that are suitable for Approximate Consistency, in which transactions “appear” to execute at the most convenient time for preventing conflicts. By leveraging Approximate Consistency for Read-Only transactions, the BifurKTM runtime system offers improved performance, application flexibility, and programmability without introducing any errors into shared memory. Our experiments show that Approximate Consistency can improve BkTM performance by up to 34x in applications with moderate network communication utilization and a read-intensive workload. Using Approximate Consistency, BkTM can reduce GPU-to-GPU network communication by 99%, reduce the number of aborts by up to 100%, and achieve an average speedup of 18x over a similarly sized CPU cluster while requiring minimal effort from the programmer.

Publication Source (Journal or Book title)

OpenAccess Series in Informatics

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