Advanced Static and Dynamic Mapping of HPC Applications on Petascale Computer Systems with GPU Accelerators
Explore the source record for details and available documents.
97 MATHEMATICS AND COMPUTING↗
Engineering topics
Datta, Kaushik
Publications and source records attributed to Datta, Kaushik.
Auto-tuning Stencil Computations on Multicore and Accelerators
The recent transformation from an environment where gains in computational performance came from increasing clock frequency and other hardware engineering innovations, to an environment where gains are realized through the deployment of ever increasing numbers of modest performance cores has profoundly changed the landscape of scientific application programming. This exponential increase in core count represents both an opportunity and a challenge: access to petascale simulation capabilities and beyond will require that this concurrency be efficiently exploited.
43 PARTICLE ACCELERATORS↗
Auto-Tuning Memory-Intensive Kernels for Multicore
In this, chapter, we discuss the optimization of three memory-intensive computational kernels — sparse matrix-vector multiplication, the Laplacian differential operator applied to structured grids, and the collision() operator with the lattice Boltzmann magnetohydrodynamics (LBMHD) application. They are all implemented using a single-process, (POSIX) threaded, SPMD model. Unlike their computationally-intense dense linear algebra cousins, performance is ultimately limited by DRAM bandwidth and the volume of data that must be transfered. To provide performance portability across current and future multicore architectures, we utilize automatic performance tuning, or auto-tuning.
97 MATHEMATICS AND COMPUTING↗