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Asynchronous Methods for Deep Reinforcement Learning

Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu

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Tags: Algorithms, Computer science, Deep learning, Machine learning, Neural networks, nVidia, Performance, Tesla K40

February 6, 2016 by hgpu

EIE: Efficient Inference Engine on Compressed Deep Neural Network

Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark A. Horowitz, William J. Dally

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Tags: Computer science, Computer vision, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia Jetson TK1, Sparse matrix

February 6, 2016 by hgpu

Optimization and Large Scale Computation of an Entropy-Based Moment Closure

C. Kristopher Garrett, Cory Hauck, Judith Hill

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Tags: Algorithms, CUDA, Heterogeneous systems, nVidia, OpenMP, Physics, Tesla K20

February 3, 2016 by hgpu

Deep Learning For Smile Recognition

Patrick O. Glauner

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Tags: Computer science, Computer vision, CUDA, Deep learning, Machine learning, Neural networks, nVidia, Tesla K40

February 3, 2016 by hgpu

A novel approach to evaluating compact finite differences and similar tridiagonal schemes on GPU-accelerated clusters

Ashwin Trikuta Srinath

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Tags: Algorithms, Computer science, CUDA, Finite difference, Fluid dynamics, GPU cluster, Numerical simulation, nVidia, OpenMPI, Tesla K20, Tesla K40, Thesis

February 3, 2016 by hgpu

Algorithms and Heuristics for Scalable Betweenness Centrality Computation on Multi-GPU Systems

Flavio Vella, Giancarlo Carbone, Massimo Bernaschi

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Tags: Algorithms, Computer science, CUDA, Data Structures and Algorithms, Graph theory, nVidia, OpenMPI, Tesla K20, Tesla K80

February 3, 2016 by hgpu

Edge coloring in unstructured CFD codes

Andrew Giuliani, Lilia Krivodonova

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Tags: Algorithms, Computer science, Fluid dynamics, nVidia, nVidia GeForce GTX 580

February 3, 2016 by hgpu

Fast, Realistic Terrain Synthesis

Justin Crause

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Tags: 3D Graphics and Realism, Computer science, CUDA, nVidia, nVidia GeForce GTX 660 Ti, Thesis

February 2, 2016 by hgpu

Extracting Flow Features Using Bag-of-Features and Supervised Learning Techniques

Yifei Li

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Tags: Algorithms, Clustering, Computer science, Computer vision, CUDA, Machine learning, nVidia, nVidia GeForce GTX 580, OpenMP, Package, Thesis, Visualization

February 2, 2016 by hgpu

GPGPU Multi Object Bayesian Tracking with an embedded System on a Chip

David Webb

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Tags: Bayesian, Computer science, Computer vision, CUDA, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 960, nVidia Jetson TK1, SoC, Thesis

February 2, 2016 by hgpu

A power-aware symbiotic scheduling algorithm for concurrent GPU kernels

Teng Li, Vikram K. Narayana, Tarek El-Ghazawi

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Tags: Algorithms, Computer science, CUDA, nVidia, Performance, Task scheduling, Tesla K20

February 1, 2016 by hgpu

Accelerating the Critical Line Algorithm for Portfolio Optimization Using GPUs

Raja H. Singh, Lee Barford, Frederick Harris Jr.

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Tags: Algorithms, Computer science, CUDA, Finance, nVidia, nVidia GeForce GTX 780

February 1, 2016 by hgpu

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Introduction to Computer Vision

ACT: Accelerator Compiler Toolkit

Automatically Generating ML Compiler Backends from Tensor Accelerator ISA Descriptions

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

EinsummableV3 - graph-based computation with tensor operations support

Every Kernel Is a Join: Automatic Multi-GPU Parallelism for AI Computations in Einsummable

MaxKernel: Intelligent TPU Kernel Engineering Agents

MaxKernel: Agentic Kernel Generation for TPUs

See all packages

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high performance computing on graphics processing units: hgpu.org

Asynchronous Methods for Deep Reinforcement Learning

EIE: Efficient Inference Engine on Compressed Deep Neural Network

Optimization and Large Scale Computation of an Entropy-Based Moment Closure

Deep Learning For Smile Recognition

A novel approach to evaluating compact finite differences and similar tridiagonal schemes on GPU-accelerated clusters

Algorithms and Heuristics for Scalable Betweenness Centrality Computation on Multi-GPU Systems

Edge coloring in unstructured CFD codes

Fast, Realistic Terrain Synthesis

Extracting Flow Features Using Bag-of-Features and Supervised Learning Techniques

GPGPU Multi Object Bayesian Tracking with an embedded System on a Chip

A power-aware symbiotic scheduling algorithm for concurrent GPU kernels

Accelerating the Critical Line Algorithm for Portfolio Optimization Using GPUs

Recent source codes

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Introduction to Computer Vision

ACT: Accelerator Compiler Toolkit

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

EinsummableV3 - graph-based computation with tensor operations support

MaxKernel: Intelligent TPU Kernel Engineering Agents

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