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CRINK: Automatic CUDA code generation for affine C programs

Akanksha Singh

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Tags: Code generation, Computer science, CUDA, nVidia, Tesla C1060, Thesis

August 10, 2015 by hgpu

Towards Distortion-Predictable Embedding of Neural Networks

Axel Angel

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Tags: Caffe, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, Package, Thesis

August 7, 2015 by hgpu

DenseCut: Densely Connected CRFs for Realtime GrabCut

M. M. Cheng, V. A. Prisacariu, S. Zheng, P. H. S. Torr, C. Rother

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Tags: Computer vision, CUDA, Image processing, nVidia, Package, Tesla K40

August 7, 2015 by hgpu

Optimising Reconfigurable Systems for Real-time Applications

Thomas Chun Pong Chau

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Tags: Algorithms, Computer science, CUDA, FPGA, Machine learning, Mixed precision, nVidia, Package, Tesla C2070, Thesis

August 7, 2015 by hgpu

Real-Time Pedestrian Detection With Deep Networks Cascades

Anelia Angelova, Alex Krizhevsky, Vincent Vanhoucke, Abhijit Ogale, Dave Ferguson

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Tags: Algorithms, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, Package, Tesla K20

August 6, 2015 by hgpu

Exploiting two-level parallelism by aggregating computing resources in task-based applications over accelerator-based machines

Terry Cojean, Abdou Guermouche, Andra Hugo, Raymond Namyst, Pierre-Andre Wacrenier

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Tags: Computer science, CUDA, Factorization, Heterogeneous systems, Linear Algebra, nVidia, OpenMP, Task scheduling, Tesla K40

August 5, 2015 by hgpu

Flexible Linear Algebra Development and Scheduling with Cholesky Factorization

Azzam Haidar, Chongxiao Cao, Stanimire Tomov, Asim YarKhan, Piotr Luszczek, Jack Dongarra

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Tags: Algorithms, Computer science, CUDA, Factorization, Heterogeneous systems, Intel Xeon Phi, Linear Algebra, nVidia, Tesla M2090

August 5, 2015 by hgpu

Dense Symmetric Indefinite Factorization on GPU Accelerated Architectures

Marc Baboulin, Jack Dongarra, Adrien Remy, Stanimire Tomov, Ichitaro Yamazaki

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Tags: Algorithms, Computer science, CUDA, Factorization, Linear Algebra, nVidia, Tesla K40

August 5, 2015 by hgpu

Face Search at Scale: 80 Million Gallery

Dayong Wang, Charles Otto, Anil K. Jain

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Tags: Computer science, Computer vision, CUDA, Deep learning, Face detection, nVidia

August 3, 2015 by hgpu

Multi-GPU Parallel Computing and Task Scheduling under Virtualization

Yujie Zhang, Jiabin Yuan, Xiangwen Lu, Xingfang Zhao

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Tags: Computer science, CUDA, nVidia, OpenMP, Task scheduling, Tesla C2050, Virtualization

August 3, 2015 by hgpu

A Data-oriented Method for Scheduling Dependent Tasks on High-density Multi-GPU Systems

Peng Zhang, Yuxiang Gao, Meikang Qiu

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Tags: Algorithms, Computer science, CUDA, Matrix inversion, Matrix multiplication, nVidia, Task scheduling, Tesla K40

August 3, 2015 by hgpu

Parallel Surface Reconstruction on GPU

Heng Gao, Jie Tang, Gangshan Wu

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 660

August 1, 2015 by hgpu

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

Agentic Code Optimization via Compiler-LLM Cooperation

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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high performance computing on graphics processing units: hgpu.org

Programming

CRINK: Automatic CUDA code generation for affine C programs

Towards Distortion-Predictable Embedding of Neural Networks

DenseCut: Densely Connected CRFs for Realtime GrabCut

Optimising Reconfigurable Systems for Real-time Applications

Real-Time Pedestrian Detection With Deep Networks Cascades

Exploiting two-level parallelism by aggregating computing resources in task-based applications over accelerator-based machines

Flexible Linear Algebra Development and Scheduling with Cholesky Factorization

Dense Symmetric Indefinite Factorization on GPU Accelerated Architectures

Face Search at Scale: 80 Million Gallery

Multi-GPU Parallel Computing and Task Scheduling under Virtualization

A Data-oriented Method for Scheduling Dependent Tasks on High-density Multi-GPU Systems

Parallel Surface Reconstruction on GPU

Recent source codes

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

Agentic Code Optimization via Compiler-LLM Cooperation

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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