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Cytochrome P450 site of metabolism prediction from 2D topological fingerprints using GPU accelerated probabilistic classifiers

J.D. Tyzack, H.Y. Mussa, M.J. Williamson,J. Kirchmair, R.C. Glen.

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Tags: Biochemistry, CUDA, Machine learning, nVidia, nVidia GeForce GT 640, nVidia Tesla C2075

May 31, 2014 by hgpu

sycl-bench: SYCL Benchmark Suite

SYCL-Bench: A Versatile Cross-Platform Benchmark Suite for Heterogeneous Computing

Ginkgo: A Modern Linear Operator Algebra Framework for High Performance Computing

Ginkgo: A Modern Linear Operator Algebra Framework for High Performance Computing

Ginkgo: a high-performance linear algebra library for manycore systems

Preparing Ginkgo for AMD GPUs – A Testimonial on Porting CUDA Code to HIP

GROMACS with CUDA-aware MPI direct GPU communication support

Heterogeneous Parallelization and Acceleration of Molecular Dynamics Simulations in GROMACS

Rodinia_SYCL: The Rodinia Benchmark Suite in SYCL

The Rodinia Benchmark Suite in SYCL

Neural Architecture Search Without Training

Neural Architecture Search without Training

OpenABLext: An extension to OpenABL, a framework to generate high- performance code for agent-based simulations

OpenABLext: An automatic code generation framework for agent-based simulations on CPU-GPU-FPGA heterogeneous platforms

SOFF: SNU OpenCL Framework for FPGAs

SOFF: An OpenCL High-Level Synthesis Framework for FPGAs

GCOOSpDM: Sparse-dense matrix-matrix multiplication on GPUs

Efficient Sparse-Dense Matrix-Matrix Multiplication on GPUs Using the Customized Sparse Storage Format

Programming Your GPU with OpenMP: A Hands-On Introduction

Evaluating the performance of HPC-style SYCL applications

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2020

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Naha, Okinawa, Japan

The Fifth International Workshop on GPU Computing and AI (GCA), 2020

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