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Sparse Matrix-Matrix Multiplication on Multilevel Memory Architectures: Algorithms and Experiments

Deveci, Mehmet D.; Hammond, Simon D.; Wolf, Michael W.; Rajamanickam, Sivasankaran R.

Architectures with multiple classes of memory media are becoming a common part of mainstream supercomputer deployments. So called multi-level memories offer differing characteristics for each memory component including variation in bandwidth, latency and capacity. This paper investigates the performance of sparse matrix multiplication kernels on two leading highperformance computing architectures — Intel's Knights Landing processor and NVIDIA's Pascal GPU. We describe a data placement method and a chunking-based algorithm for our kernels that exploits the existence of the multiple memory spaces in each hardware platform. We evaluate the performance of these methods w.r.t. standard algorithms using the auto-caching mechanisms Our results show that standard algorithms that exploit cache reuse performed as well as multi-memory-aware algorithms for architectures such as Ki\iLs where the memory subsystems have similar latencies. However, for architectures such as GPUS where memory subsystems differ significantly in both bandwidth and latency, multi-memory-aware methods are crucial for good performance. In addition, our new approaches permit the user to run problems that require larger capacities than the fastest memory of each compute node without depending on the software-managed cache mechanisms.

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TYPE Other Report YEAR 2018
DOIOSTI

Profiling and Debugging Support for the Kokkos Programming Model

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Hammond, Simon D.; Trott, Christian R.; Ibanez-Granados, Daniel A.; Sunderland, Daniel S.

Supercomputing hardware is undergoing a period of significant change. In order to cope with the rapid pace of hardware and, in many cases, programming model innovation, we have developed the Kokkos Programming Model – a C++-based abstraction that permits performance portability across diverse architectures. Our experience has shown that the abstractions developed can significantly frustrate debugging and profiling activities because they break expected code proximity and layout assumptions. In this paper we present the Kokkos Profiling interface, a lightweight, suite of hooks to which debugging and profiling tools can attach to gain deep insights into the execution and data structure behaviors of parallel programs written to the Kokkos interface.

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TYPE Conference Poster YEAR 2018
DOIOSTIScopus
Results 76–100 of 266
Results 76–100 of 266