high performance computing on graphics processing units: hgpu.org

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Out-of-core Training for Extremely Large-Scale Neural Networks With Adaptive Window-Based Scheduling

Akio Hayakawa, Takuya Narihira

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Tags: Algorithms, Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, Tesla V100

November 1, 2020 by hgpu

Not Half Bad: Exploring Half-Precision in Graph Convolutional Neural Networks

John Brennan, Stephen Bonner, Amir Atapour-Abarghouei, Philip T Jackson, Boguslaw Obara, Andrew Stephen McGough

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Tags: CNN, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia Titan RTX, PyTorch, Tesla P100, Tesla V100

November 1, 2020 by hgpu

Applications of Deep Neural Networks

Jeff Heaton

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Tags: Book, Computer science, Computer vision, Deep learning, Neural networks, NLP, Package, Python, Security

September 20, 2020 by hgpu

Montage: A Neural Network Language Model-Guided JavaScript Engine Fuzzer

Suyoung Lee, HyungSeok Han, Sang Kil Cha, Sooel Son

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Tags: Computer science, Java, Neural networks, nVidia, nVidia GeForce GTX Titan XP, Package, Python, PyTorch, Security

August 30, 2020 by hgpu

Darknet on OpenCL: a multi-platform tool for object detection and classification

Piotr Sowa, Jacek Izydorczyk

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Tags: ARM, Artificial intelligence, Benchmarking, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia Titan RTX, OpenCL, Package

July 26, 2020 by hgpu

EDSSA: An Encoder-Decoder Semantic Segmentation Networks Accelerator on OpenCL-Based FPGA Platform

Hongzhi Huang, Yakun Wu, Mengqi Yu, Xuesong Shi, Fei Qiao, Li Luo, Qi Wei, Xinjun Liu

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Tags: Algorithm optimization, Computer science, Computer vision, FPGA, Neural networks, OpenCL

July 26, 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

Investigating Input Representations and Representation Models of Source Code for Machine Learning

Alexander Brauckmann

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Tags: AMD Radeon HD 7970, ATI, Code generation, Computer science, LLVM, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX 970, OpenCL, Thesis

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

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

Unsupervised Deep Learning of Incompressible Fluid Dynamics

Nils Wandel, Michael Weinmann, Reinhard Klein

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Tags: Deep learning, Differential equations, Fluid dynamics, Fluid simulation, Neural networks, nVidia, nVidia GeForce GTX 2080 Ti, Partial differential equations, PDEs, PyTorch

June 21, 2020 by hgpu

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Accelerating a Linear Programming Algorithm on AMD GPUs

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Towards Efficient and Practical GPU Multitasking in the Era of LLM

Bandicoot: C++ library for GPU accelerated linear algebra

Bandicoot: A Templated C++ Library for GPU Linear Algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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high performance computing on graphics processing units: hgpu.org

Out-of-core Training for Extremely Large-Scale Neural Networks With Adaptive Window-Based Scheduling

Not Half Bad: Exploring Half-Precision in Graph Convolutional Neural Networks

Applications of Deep Neural Networks

Montage: A Neural Network Language Model-Guided JavaScript Engine Fuzzer

Darknet on OpenCL: a multi-platform tool for object detection and classification

EDSSA: An Encoder-Decoder Semantic Segmentation Networks Accelerator on OpenCL-Based FPGA Platform

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

Deep Graph Library Optimizations for Intel(R) x86 Architecture

Investigating Input Representations and Representation Models of Source Code for Machine Learning

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

Sparse GPU Kernels for Deep Learning

Unsupervised Deep Learning of Incompressible Fluid Dynamics

Recent source codes

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Bandicoot: C++ library for GPU accelerated linear algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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