Design, Implementation and Performance Evaluation of an Elastic Partitioned Global Address Space Storage (EPGASS)

Abstract

Advances in modern technologies, high-speed and more precise instrumentation for data acquisition, cutting-edge high-performance computing, improved simulation modelling, and the development of large data centres have collectively contributed to the rapid accumulation of massive datasets, commonly referred to as Big Data. While the processing speed of CPU/GPGPU in high-performance computing has steadily increased, the rate of data input and output (I/O), even with parallel I/O, has lagged behind the advances in computing speeds. Although a number of computing techniques have been adopted to bridge the CPU I/O gap, I/O still remains a major bottleneck to high performance data processing and large scale scientific applications. Recent advances in database management and data processing technologies focus on in-memory (or DRAM) data storage system to enhance performance. The challenge, however, lies in developing an I/O subsystem infrastructure that can match the computational speeds of advanced computing systems operating at the petascale and exascale levels. The thesis proposes an I/O subsystem infrastructure leveraging in-memory computing to meet the computational speeds of advanced computing systems at the petascale and exascale level, leveraging the Partitioned Global Address Space Model (PGAS) of computing on commodity hardware. The PGAS system is used to host an in-memory data store, providing high performance access to in-memory data. The proposed architecture is reminiscent of that of RAMCloud, but with some major differences. The architecture consists of a number of commodity nodes, that can grow into hundreds or thousands, whose physical memories are aggregated as a single global logical address space, with a fraction of these nodes serving as the I/O nodes, while the remaining nodes form the collection of compute nodes. The I/O nodes maintain in-memory data in the global address space and run thousands of threads, some of which persist the memory resident data onto Solid-State Device (SSDs), attached to the nodes. The thesis refers to this architecture as EPGASS: An Elastic Partitioned Global Address Space Storage. The major contribution of the thesis is the design, implementation, and application usage EPGASS. The study presents strategies to address challenges such as memory-to memory data copying, fault-tolerance, long-term persistence with asynchronous parallel data migration from memory-to-SSDs and vice versa and also from SSDs-to-hard disks (HDD), with a hierarchical storage approach. The thesis also discusses use-cases with diverse applications, including a novel approach to dense matrix-matrix multiplication and multidimensional index schemes, which minimise the communication overhead to improve algorithmic efficiency.