Design of a large-scale storage-class RRAM system
Proceedings of the 27th international ACM conference on International conference on supercomputing
Exploring the future of out-of-core computing with compute-local non-volatile memory
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Triple-A: a Non-SSD based autonomic all-flash array for high performance storage systems
Proceedings of the 19th international conference on Architectural support for programming languages and operating systems
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The emergence of high performance computing (HPC) platforms equipped with solid state drives (SSD) presents an opportunity to dramatically increase the efficiency of out-of-core numerical linear algebra computations. In this paper, we explore the advantages and challenges associated with performing sparse matrix vector multiplications (SpMV) on a small SSD test bed. Such an endeavor requires programming abstractions that ease implementation, while enabling an efficient usage of the resources in the test bed. For this purpose, we adopt a task-based out-of-core programming model on top of a dataflow middleware based on the filter stream programming model. We compare the performance of the resulting out-of-core iterated SpMV procedure running on the SSD test bed to the performance of an in-core implementation on a multi-core cluster for solving large-scale eigen value problems. Preliminary experiments indicate that the out-of-core implementation on the SSD test bed can compete with an in-core implementation in terms of the total CPU-hour cost. We conclude with some architectural design suggestions that can enable numerical linear algebra computations in general to be carried out with high efficiency on SSD-equipped platforms.