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Sparse matrix partitioning for optimizing SpMV on CPU-GPU heterogeneous platforms

Akrem Benatia, Weixing Ji, Yizhuo Wang, Feng Shi

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX Titan X, Sparse matrix

January 5, 2020 by hgpu

Towards Unified INT8 Training for Convolutional Neural Network

Feng Zhu, Ruihao Gong, Fengwei Yu, Xianglong Liu, Yanfei Wang, Zhelong Li, Xiuqi Yang, Junjie Yan

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Tags: Computer science, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti

January 5, 2020 by hgpu

LLVM-based automation of memory decoupling for OpenCL applications on FPGAs

Arnab A Purkayastha, Samuel Rogers, Suhas A Shiddhibhabi, Hamed Tabkhi

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Tags: Computer science, FPGA, LLVM, OpenCL, Package

January 5, 2020 by hgpu

Abstractions for Programming Graphics Processors in High-Level Programming Languages

Tim Besard

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Tags: Computer science, CUDA, Heterogeneous systems, High-level Languages, LLVM, nVidia, nVidia GeForce GTX 1080, Package, Thesis

December 29, 2019 by hgpu

Porting tree-based hash table compression to GPGPU model checking

Danny Bergsma

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Tags: Algorithms, Computer science, CUDA, Hashing, nVidia, nVidia GeForce GTX Titan XP, Package, Thesis

December 29, 2019 by hgpu

Automatic Performance Optimisation of Parallel Programs for GPUs via Rewrite Rules

Toomas Remmelg

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Tags: AMD Radeon R9 295X2, ARM, ATI, Computer science, nVidia, nVidia GeForce GTX Titan Black, OpenCL, Performance, performance portability, Thesis

December 29, 2019 by hgpu

Accelerating Molecular Docking by Parallelized Heterogeneous Computing – A Case Study of Performance, Quality of Results, and Energy-Efficiency using CPUs, GPUs, and FPGAs

Leonardo Solis Vasquez

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Tags: AMD Radeon RX Vega 56, ATI, Computer science, FPGA, Heterogeneous systems, Macromolecule, nVidia, OpenCL, Package, Physics, Tesla V100, Thesis

December 29, 2019 by hgpu

libmolgrid: GPU Accelerated Molecular Gridding for Deep Learning Applications

Jocelyn Sunseri, David Ryan Koes

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Tags: Chemistry, Computer science, CUDA, Deep learning, Machine learning, Molecular modeling, Neural networks, nVidia, nVidia GeForce GTX Titan V, nVidia GeForce GTX Titan X, Package, PyTorch, TensorFlow

December 15, 2019 by hgpu

Array Languages Make Neural Networks Fast

Artjoms Šinkarovs, Hans-Nikolai Vießmann, Sven-Bodo Scholz

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Tags: Computer science, CUDA, High-level Languages, nVidia, Package, Python, PyTorch, TensorFlow, Tesla K20

December 15, 2019 by hgpu

The BondMachine toolkit: Enabling Machine Learning on FPGA

Mirko Mariotti, Loriano Storchi, Daniele Spiga, Davide Salomoni, Tommaso Boccali, Daniele Bonacorsi

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Tags: Computer science, FPGA, Go, Machine learning

December 8, 2019 by hgpu

PyTorch: An Imperative Style, High-Performance Deep Learning Library

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, Soumith Chintala

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Tags: Benchmarking, Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia Quadro GP100, Package, Python, PyTorch

December 8, 2019 by hgpu

Masivo: Parallel Simulation Model Based on OpenCL for Massive Public Transportation Systems’ Routes

Juan Ruiz-Rosero, Gustavo Ramirez-Gonzalez, Rahul Khanna

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Tags: Computer science, OpenCL, Package

December 8, 2019 by hgpu

SeedFold: Scaling Biomolecular Structure Prediction

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

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