Automatic Generation of Multicore Chemical Kernels

Abstract

This work presents the Kinetics PreProcessor: Accelerated (KPPA), a general analysis and code generation tool that achieves significantly reduced time-to-solution for chemical kinetics kernels on three multicore platforms: NVIDIA GPUs using CUDA, the Cell Broadband Engine, and Intel Quad-Core Xeon CPUs. A comparative performance analysis of chemical kernels from WRFChem and the Community Multiscale Air Quality Model (CMAQ) is presented for each platform in double and single precision on coarse and fine grids. We introduce the multicore architecture parameterization that KPPA uses to generate a chemical kernel for these platforms and describe a code generation system that produces highly tuned platform-specific code. Compared to state-of-the-art serial implementations, speedups exceeding 25Â are regularly observed, with a maximum observed speedup of 41:1Â in single precision.

Chemical Mechanism Solvers in Air Quality Models

“…This section presents parallelization strategies for Rosenbrock integration in one-cell-per-thread, N-cells-per-thread, and 4/2-cells-per-thread decompositions. For performance benchmarks of parallelized Rosenbrock solvers, see [32][33][34]62].…”

“…KPPA (the Kinetics PreProcessor: Accelerated) [32], is the next generation KPP tool that achieves significantly reduced time-to-solution for chemical kinetics kernels on both traditional and emerging architectures. In addition to the basic KPP functionality, KPPA generates OpenMP code with SSE or Alitivec for traditional CPUs, CUDA code for NVIDIA GPUs, and optimized C codes for the Cell Broadband Engine Architecture (CBEA), in either double or single precision.…”

“…Code of this caliber typically requires meticulous hand-optimization, but KPPA is able to generate this code automatically in seconds. See [32] for further performance analysis of KPPA.…”

“…An early approach to exploit parallelism was through vectorization [21], where each instruction line of the chemical solver acts repeatedly on different data items associated with different cells. This idea has found renewed interest due to modern accelerator architectures such as the Cell Broadband Engine and general purpose graphical processing units (GPGPUs) [32][33][34][35].…”

Improving Utility of GPU in Accelerating Industrial Applications With User-Centered Automatic Code Translation

“…To date, numerous automatic CPU-to-GPU source parallelization translation tools [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27], including algorithmic skeleton based [14][15][16], polyhedral model based [9][10][11][12][13], or directive based [17][18][19][20][21][22][23] have been developed for academic and commercial use. While their acceleration is promising, utilizing them by normal users in general real-word applications is still challenging.…”