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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

A Brief Description of the Kokkos implementation of the SNAP potential in ExaMiniMD

Thompson, Aidan P.; Trott, Christian R.

Within the EXAALT project, the SNAP [1] approach is being used to develop high accuracy potentials for use in large-scale long-time molecular dynamics simulations of materials behavior. In particular, we have developed a new SNAP potential that is suitable for describing the interplay between helium atoms and vacancies in high-temperature tungsten[2]. This model is now being used to study plasma-surface interactions in nuclear fusion reactors for energy production. The high-accuracy of SNAP potentials comes at the price of increased computational cost per atom and increased computational complexity. The increased cost is mitigated by improvements in strong scaling that can be achieved using advanced algorithms [3].

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TYPE Other Report YEAR 2017
DOIDOIOSTIOSTI

Performance-portable sparse matrix-matrix multiplication for many-core architectures

Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017

Deveci, Mehmet D.; Trott, Christian R.; Rajamanickam, Sivasankaran R.

We consider the problem of writing performance portablesparse matrix-sparse matrix multiplication (SPGEMM) kernelfor many-core architectures. We approach the SPGEMMkernel from the perspectives of algorithm design and implementation, and its practical usage. First, we design ahierarchical, memory-efficient SPGEMM algorithm. We thendesign and implement thread scalable data structures thatenable us to develop a portable SPGEMM implementation. We show that the method achieves performance portabilityon massively threaded architectures, namely Intel's KnightsLanding processors (KNLs) and NVIDIA's Graphic ProcessingUnits (GPUs), by comparing its performance to specializedimplementations. Second, we study an important aspectof SPGEMM's usage in practice by reusing the structure ofinput matrices, and show speedups up to 3× compared to thebest specialized implementation on KNLs. We demonstratethat the portable method outperforms 4 native methods on2 different GPU architectures (up to 17× speedup), and it ishighly thread scalable on KNLs, in which it obtains 101× speedup on 256 threads.

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TYPE Conference Poster YEAR 2017
DOIOSTIScopus
Results 101–125 of 203
Results 101–125 of 203