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A Similarity Measure for GPU Kernel Subgraph Matching

Robert V. Lim, Boyana Norris, Allen D. Malony

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Tags: Computer science, CUDA, nVidia, Performance, Tesla K80, Tesla M40, Tesla P100

July 14, 2017 by hgpu

A Fast Method For Computing Principal Curvatures From Range Images

Andrew Spek, Wai Ho Li, Tom Drummond

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Tags: Computer science, Computer vision, CUDA, nVidia, Package, Tesla K40

July 5, 2017 by hgpu

A Linear Algebra Approach to Fast DNA Mixture Analysis Using GPUs

Siddharth Samsi, Brian Helfer, Jeremy Kepner, Albert Reuther, Darrell O. Ricke

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Tags: Algorithms, Computer science, CUDA, Genomics, Linear Algebra, Matrix multiplication, nVidia, Performance, Tesla K80

July 5, 2017 by hgpu

Parle: parallelizing stochastic gradient descent

Pratik Chaudhari, Carlo Baldassi, Riccardo Zecchina, Stefano Soatto, Ameet Talwalkar

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Tags: Algorithms, Computer science, CUDA, Deep learning, Lua, Machine learning, nVidia, Package, Performance, Torch

July 5, 2017 by hgpu

Deep neural networks for direct, featureless learning through observation: the case of 2d spin models

K. Mills, I. Tamblyn

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Tags: Condensed matter, CUDA, Deep learning, Ising model, Materials Science, Neural networks, nVidia, Phase transition, Physics, Tesla K40

July 2, 2017 by hgpu

High-Performance Out-of-core Block Randomized Singular Value Decomposition on GPU

Yuechao Lu, Fumihiko Ino, Yasuyuki Matsushita

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Tags: Algorithms, Computer science, Computer vision, CUDA, Linear Algebra, Machine learning, nVidia, Tesla P100

June 25, 2017 by hgpu

Panda: A Compiler Framework for Concurrent CPU-GPU Execution of 3D Stencil Computations on GPU-accelerated Supercomputers

Mohammed Sourouri, Scott B. Baden, Xing Cai

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Tags: Code generation, Compilers, Computer science, CUDA, GPU cluster, Heterogeneous systems, MPI, nVidia, OpenMP, Tesla K20

June 21, 2017 by hgpu

Rgtsvm: Support Vector Machines on a GPU in R

Zhong Wang, Tinyi Chu, Lauren A Choate, Charles G Danko

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Tags: Computer science, CUDA, Machine learning, nVidia, Package, R, Tesla K20

June 21, 2017 by hgpu

Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras

Keunwoo Choi, Deokjin Joo, Juho Kim

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Tags: Computer science, CUDA, Deep learning, Keras, Neural networks, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX Titan X, Package, Python, Signal processing, Tesla K80, Tesla M60

June 21, 2017 by hgpu

Device Placement Optimization with Reinforcement Learning

Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi, Samy Bengio, Jeff Dean

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Tags: Artificial intelligence, Computer science, CUDA, Deep learning, Heterogeneous systems, Neural networks, nVidia, TensorFlow, Tesla K80

June 17, 2017 by hgpu

Non-Hydrostatic Pressure Shallow Flows: GPU Implementation Using Finite-Volume and Finite-Difference Scheme

C. Escalante, T. Morales de Luna, M.J. Castro

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Tags: Algorithms, CUDA, Finite difference, Fluid dynamics, Mathematics, Numerical Analysis, nVidia

June 17, 2017 by hgpu

Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Priya Goyal, Piotr Dollar, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, Kaiming He

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Tags: Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, Package, Tesla P100

June 10, 2017 by hgpu

pytest-gpu-proof: A Simple Pytest Extension for Local GPU Code Testing

pytest-gpu-proof: Enabling Cloud-CPU Continuous Integration for GPU Code with Local GPU Attestation

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

AutoTuneBench: Trustworthy Measurement for Agent Auto-Tuning of LLM Serving Engines

nvsass-disassembler

Automated Instruction Encoding Synthesis for Modern GPU ISA Compression

PrefixBench-H100: Characterizing Prefix Reuse and Time-to-First-Token in H100 LLM Serving

PTXBench: an open-source environment for kernel agents with architecture-specific CUDA/PTX

PTXBench: Benchmark and Adapt LLMs for GPU Kernel Optimization with Architecture-specific PTX

RealisticTritonBench: A Benchmark for Triton-Kernel Generation in Real-World AI Frameworks

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high performance computing on graphics processing units: hgpu.org

Programming

A Similarity Measure for GPU Kernel Subgraph Matching

A Fast Method For Computing Principal Curvatures From Range Images

A Linear Algebra Approach to Fast DNA Mixture Analysis Using GPUs

Parle: parallelizing stochastic gradient descent

Deep neural networks for direct, featureless learning through observation: the case of 2d spin models

High-Performance Out-of-core Block Randomized Singular Value Decomposition on GPU

Panda: A Compiler Framework for Concurrent CPU-GPU Execution of 3D Stencil Computations on GPU-accelerated Supercomputers

Rgtsvm: Support Vector Machines on a GPU in R

Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras

Device Placement Optimization with Reinforcement Learning

Non-Hydrostatic Pressure Shallow Flows: GPU Implementation Using Finite-Volume and Finite-Difference Scheme

Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Recent source codes

pytest-gpu-proof: A Simple Pytest Extension for Local GPU Code Testing

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

AutoTuneBench: Trustworthy Measurement for Agent Auto-Tuning of LLM Serving Engines

nvsass-disassembler

PrefixBench-H100: Characterizing Prefix Reuse and Time-to-First-Token in H100 LLM Serving

FP4 FlashAttention forward, backward, and TorchTitan reproduction code

EinsummableV3 - graph-based computation with tensor operations support

MaxKernel: Intelligent TPU Kernel Engineering Agents

PTXBench: an open-source environment for kernel agents with architecture-specific CUDA/PTX

RealisticTritonBench: A Benchmark for Triton-Kernel Generation in Real-World AI Frameworks

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