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OpenACC cache Directive: Opportunities and Optimizations

Ahmad Lashgar, Amirali Baniasadi

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Tags: Code generation, Computer science, CUDA, nVidia, OpenACC, OpenCL, Package, Performance, Tesla K20

December 3, 2016 by hgpu

GeantV: from CPU to accelerators

G. Amadio, A. Ananya, J. Apostolakis, A. Arora, M. Bandieramonte, A. Bhattacharyya, C. Bianchini, R. Brun, P. Canal, F. Carminati, L. Duhem, D. Elvira, A. Gheata, M. Gheata, I. Goulas, R. Iope, S. Jun, G. Lima, A. Mohanty, T. Nikitina, M. Novak, W. Pokorski, A. Ribon, R. Sehgal, O. Shadura, S. Vallecorsa, S. Wenzel, Y. Zhang

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Tags: CUDA, HEP, High Energy Physics - Phenomenology, Intel Xeon Phi, nVidia, Physics, Tesla K20

December 3, 2016 by hgpu

Efficient Kernel Synthesis for Performance Portable Programming

Li-Wen Chang, Izzat El Hajj, Christopher Rodrigues, Juan Gomez-Luna, Wen-mei Hwu

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Tags: Code generation, Computer science, CUDA, Design space exploration, Heterogeneous systems, nVidia, performance portability, Tesla C2050, Tesla K20

November 25, 2016 by hgpu

High performance pattern matching and data remanence on graphics processing units

X. Bellekens

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Tags: Aho Corasick, Algorithms, amazon, BMH, CUDA, Digital Forensic, forensic, GPU, pattern matching, PFAC, remanence, Sequence matching, String matching, Suffix Tree, Tesla K20, Tesla K20c

November 22, 2016 by xhgpu

GPU-accelerated Red Blood Cells Simulations with Transport Dissipative Particle Dynamics

Ansel L. Blumers, Yu-Hang Tang, Zhen Li, Xuejin Li, George E. Karniadakis

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Tags: Biological Physics, Computational Physics, CUDA, MPI, nVidia, Package, Physics, Tesla K20

November 22, 2016 by hgpu

HIPAcc: A Domain-Specific Language and Compiler for Image Processing

Richard Membarth, Oliver Reiche, Frank Hannig, Jurgen Teich, Mario Korner, Wieland Eckert

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Tags: Algorithms, AMD Radeon R9 290X, ARM, ATI, Compilers, Computer science, CUDA, Heterogeneous systems, Image processing, Intel Xeon Phi, nVidia, OpenCL, Package, Tesla K20

November 19, 2016 by hgpu

How to scale distributed deep learning?

Peter H. Jin, Qiaochu Yuan, Forrest Iandola, Kurt Keutzer

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Tags: Computer science, CUDA, Deep learning, GPU cluster, nVidia, Performance, Tesla K20, Tesla K80

November 19, 2016 by hgpu

OpenCL-based optimizations for acceleration of object tracking on FPGAs and GPUs

Amir Momeni, Hamed Tabkhi, Gunar Schirner, David Kaeli

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Tags: Algorithms, Computer science, FPGA, Heterogeneous systems, nVidia, OpenCL, Tesla K20

November 13, 2016 by hgpu

Shuffle Reduction Based Sparse Matrix-Vector Multiplication on Kepler GPU

Yuan Tao, Huang Zhi-Bin

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Tags: Algorithms, Computer science, CUDA, nVidia, Sparse matrix, Tesla K20

November 10, 2016 by hgpu

Performance Portability of the Aeras Atmosphere Model to Next Generation Architectures using Kokkos

Jerry Watkins, Irina Tezaur

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Tags: CUDA, Earth and Space Sciences, MPI, nVidia, OpenMP, performance portability, Physics, Tesla K20, Tesla K80

November 8, 2016 by hgpu

Molecular Activity Prediction using Deep Learning Software Library

Yoshiki Kato, Shinji Hamada, Hitoshi Goto

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Tags: Bioinformatics, Chemistry, Computer science, CUDA, Deep learning, nVidia, Package, Python, Tesla K20

November 5, 2016 by hgpu

grim: A Flexible, Conservative Scheme for Relativistic Fluid Theories

Mani Chandra, Francois Foucart, Charles F. Gammie

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Tags: Astrophysics, Magnetohydrodynamics, nVidia, OpenCL, Package, Physics, Tesla K20

November 5, 2016 by hgpu

CLUEstering: a high-performance density-based clustering library for scientific computing

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

PyOMP: Parallel programming for CPUs and GPUs with OpenMP and Python

HiRep

Scaling SU(2) to 1000 GPUs using HiRep

GPPerf: Energy-Aware GPU Performance Prediction and Optimization Framework

Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach

gpuPairHMM: Ultra-fast GPU-based PairHMM for DNA Variant Calling

gpuPairHMM: High-speed Pair-HMM Forward Algorithm for DNA Variant Calling on GPUs

Gaia AVU-GSR

Performance portability via C++ PSTL, SYCL, OpenMP, and HIP: the Gaia AVU-GSR case study

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

Kokkidio: Fast, expressive, portable code, based on Kokkos and Eigen

LLaMA-Inference-Bench

LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators

MATAR

On a Simplified Approach to Achieve Parallel Performance and Portability Across CPU and GPU Architectures

Over-synchronization in GPU Programs

Over-synchronization in GPU Programs

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