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Compyle: a Python package for parallel computing

Aditya Bhosale, Prabhu Ramachandran

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Tags: Code generation, Computer science, CUDA, HPC, nVidia, OpenCL, Package, Python, Tesla P100

July 19, 2020 by hgpu

Accelerating Deep Learning Inference with Cross-Layer Data Reuse on GPUs

Xueying Wang, Guangli Li, Xiao Dong, Jiansong Li, Lei Liu, Xiaobing Feng

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan XP, Tesla P4

July 19, 2020 by hgpu

Deep Graph Library Optimizations for Intel(R) x86 Architecture

Sasikanth Avancha, Vasimuddin Md, Sanchit Misra, Ramanarayan Mohanty

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, Package

July 19, 2020 by hgpu

GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research

Scott LeGrand, Aaron Scheinberg, Andreas F. Tillack, Mathialakan Thavappiragasam, Josh V. Vermaas, Rupesh Agarwal, Jeff Larkin, Duncan Poole, Diogo Santos-Martins, Leonardo Solis-Vasquez, Andreas Koch, Stefano Forli, Oscar Hernandez, Jeremy C. Smith, Ada Sedova

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Tags: Biology, Biomolecules, Computational biology, CUDA, molecular docking, nVidia, nVidia DGX-2, nVidia GeForce RTX 2080 Ti, OpenCL, supercomputer, Tesla V100

July 12, 2020 by hgpu

Automated Partitioning of Data-Parallel Kernels using Polyhedral Compilation

Alexander Matz, Johannes Doerfert, Holger Fröning

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Tags: Code generation, Computer science, CUDA, LLVM, nVidia, Package, Tesla K80

July 12, 2020 by hgpu

GE-SpMM: General-purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks

Guyue Huang, Guohao Dai, Yu Wang, Huazhong Yang

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Tags: Computer science, CUDA, Linear Algebra, Matrix multiplication, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, nVidia GeForce RTX 2080, Package, Sparse matrix

July 12, 2020 by hgpu

Studies on CUDA Offloading for Real-Time Simulation and Visualization

Edgar Josafat Martinez-Noriega

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Tags: Computer science, CUDA, Image processing, nVidia, nVidia GeForce GTX 1070, nVidia GeForce GTX 970 M, nVidia GeForce RTX 2080, nVidia Jetson K1, Rendering, Thesis, Virtualization, Visualization

July 5, 2020 by hgpu

Efficient Matrix Factorization on Heterogeneous CPU-GPU Systems

Yuanhang Yu, Dong Wen, Ying Zhang, Xiaoyang Wang, Wenjie Zhang, Xuemin Lin

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Tags: Algorithms, Computer science, CUDA, Data mining, Factorization, Heterogeneous systems, Machine learning, nVidia, nVidia Quadro P 4000

July 5, 2020 by hgpu

Adaptive SpMV/SpMSpV on GPUs for Input Vectors of Varied Sparsity

Min Li, Yulong Ao, Chao Yang

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Tags: Computer science, CUDA, Machine learning, nVidia, Sparse matrix, Tesla K40, Tesla P100, Tesla V100

July 5, 2020 by hgpu

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

Hartwig Anzt, Terry Cojean, Goran Flegar, Fritz Göbel, Thomas Grützmacher, Pratik Nayak, Tobias Ribizel, Yuhsiang Mike Tsai, Enrique S. Quintana-Ortí

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Tags: Algorithms, AMD Radeon VII, ATI, Computer science, CUDA, HIP, Linear Algebra, Mathematical Software, nVidia, Package, Sparse, Sparse matrix, Tesla V100

July 5, 2020 by hgpu

GPU-based matrix-free finite element solver exploiting symmetry of elemental matrices

Utpal Kiran, Sachin Singh Gautam, Deepak Sharma

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Tags: Computer science, CUDA, FEM, Finite element method, nVidia, Sparse matrix, Tesla K40

June 28, 2020 by hgpu

Sparse GPU Kernels for Deep Learning

Trevor Gale, Matei Zaharia, Cliff Young, Erich Elsen

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Tags: Computer science, CUDA, Deep learning, Machine learning, Matrix multiplication, Neural networks, nVidia, Sparse matrix, Tesla V100

June 28, 2020 by hgpu

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

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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