论文标题
分解面向吞吐量的基因组工作负载的非易失性记忆
Disaggregating Non-Volatile Memory for Throughput-Oriented Genomics Workloads
论文作者
论文摘要
对下一代测序技术的大规模开发需要处理：大量数据和复杂的生物信息学管道。计算体系结构已经演变为解决这些问题，使几年前不可行的方法：加速器和非易失性记忆（NVM）已被广泛用于增强最苛刻的工作量。但是，在资源方面，生物信息学工作负载通常是具有不同和动态需求的较大管道的一部分。用于数据中心的软件定义基础架构（SDI）的引入提供了根源，可以显着提高基础架构管理的效率。 SDI启用了新的方法来通过分类来构建硬件资源，并提供新的硬件合成性和共享机制，以更灵活的方式部署工作负载。在本文中，我们研究了最先进的基因组学应用程序Smufin，旨在应对未来HPC设施的挑战。
Massive exploitation of next-generation sequencing technologies requires dealing with both: huge amounts of data and complex bioinformatics pipelines. Computing architectures have evolved to deal with these problems, enabling approaches that were unfeasible years ago: accelerators and Non-Volatile Memories (NVM) are becoming widely used to enhance the most demanding workloads. However, bioinformatics workloads are usually part of bigger pipelines with different and dynamic needs in terms of resources. The introduction of Software Defined Infrastructures (SDI) for data centers provides roots to dramatically increase the efficiency in the management of infrastructures. SDI enables new ways to structure hardware resources through disaggregation, and provides new hardware composability and sharing mechanisms to deploy workloads in more flexible ways. In this paper we study a state-of-the-art genomics application, SMUFIN, aiming to address the challenges of future HPC facilities.
