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Improving Performance of Iterative Applications through Interleaved Execution of Approximated CUDA Kernels

Gabriel Freytag

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Tags: Computer science, CUDA, Machine learning, Mixed precision, Neural networks, nVidia, nVidia A100, nVidia P100, Thesis

June 18, 2023 by hgpu

Reducing branch divergence to speed up parallel execution of unit testing on GPUs

Taghreed Bagies, Wei Le, Jeremy Sheafer, Ali Jannesari

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Tags: Benchmarking, Computer science, CUDA, nVidia, Package, Performance, Tesla V100

June 18, 2023 by hgpu

Efficient GPU implementation of a class of array permutations

Mathis Bouverot-Dupuis, Mary Sheeran

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Tags: Algorithms, AMD Radeon Instinct MI100, ATI, Computer science, CUDA, Haskell, nVidia, nVidia GeForce RTX 4090, Package

June 18, 2023 by hgpu

cuCatch: A Debugging Tool for Efficiently Catching Memory Safety Violations in CUDA Applications

Mohamed Tarek Ibn Ziad, Sana Damani, Aamer Jaleel, Stephen W. Keckler, Mark Stephenson

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce RTX 2080 Ti, OpenACC, OpenCL, Security

June 18, 2023 by hgpu

EfficientBioAI: Making Bioimaging AI Models Efficient in Energy, Latency and Representation

Yu Zhou, Justin Sonneck, Sweta Banerjee, Stefanie Dörr, Anika Grüneboom, Kristina Lorenz, Jianxu Chen

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Tags: Artificial intelligence, Biology, Computer science, CUDA, nVidia, nVidia Quadro RTX 4000, Package

June 18, 2023 by hgpu

GPUHarbor: Testing GPU Memory Consistency at Large

Reese Levine, Mingun Cho, Devon McKee, Andrew Quinn, Tyler Sorensen

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Tags: AMD Radeon Pro 5500M, ATI, Computer science, Memory model, nVidia, nVidia GeForce RTX 3080, nVidia Tegra TX1, OpenCL, Performance

June 11, 2023 by hgpu

Program Analysis and Machine Learning based Approach to Predict Power Consumption of CUDA Kernel

Gargi Alavani, Jineet Desai, Snehanshu Saha, Santonu Sarkar

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Tags: Benchmarking, Computer science, CUDA, Energy-efficient computing, Machine learning, nVidia, Package, Tesla K20, Tesla M60, Tesla V100

June 11, 2023 by hgpu

Accelerating 128-bit Floating-Point Matrix Multiplication on FPGAs

Fumiya Kono, Naohito Nakasato, Maho Nakata

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Tags: Benchmarking, Computer science, Extended precision, FPGA, Matrix multiplication, OpenCL

June 11, 2023 by hgpu

Hybrid CPU/GPU/APU accelerated query, insert, update and erase operations in hash tables with string keys

Tobias Groth, Sven Groppe, Thilo Pionteck, Franz Valdiek, Martin Koppehel

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Tags: APU, Computer science, CUDA, Hashing, nVidia A100, oneAPI, SYCL

June 4, 2023 by hgpu

Compiler Technologies in Deep Learning Co-Design: A Survey

Hongbin Zhang, Mingjie Xing, Yanjun Wu, Chen Zhao

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Tags: Compielrs, Computer science, Deep learning, HLS, OpenCL, Package, survey

June 4, 2023 by hgpu

Implementation Techniques for SPMD Kernels on CPUs

Joachim Meyer, Aksel Alpay, Sebastian Hack, Holger Fröning, Vincent Heuveline

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Tags: Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Package, performance portability

June 4, 2023 by hgpu

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

Zhuren Liu, Shouzhe Zhang, Justin Garrigus, Hui Zhao

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Tags: Benchmarking, Bioinformatics, Biology, Computer science, CUDA, Genomics, nVidia, nVidia GeForce RTX 3070, Package

May 28, 2023 by hgpu

GPUODEBenchmarks: Comparsion of Julia's GPU Kernel based ODE solvers with other open-source GPU ODE solvers

Automating Heterogeneous Parallelism in Numerical Differential Equations

Dat3M: Memory Model Aware Verification

Towards Unified Analysis of GPU Consistency

NVIDIA Federated Learning Application Runtime Environment

Supercharging Federated Learning with Flower and NVIDIA FLARE

Chat AI: A Seamless Slurm-Native Solution for HPC-Based Services

PSCToolkit: solving sparse linear systems with a large number of GPUs

CATBench: Benchmarking Framework

CATBench: A Compiler Autotuning Benchmarking Suite for Black-box Optimization

General-purpose Polyhedral Compilers

A Survey of General-purpose Polyhedral Compilers

Optimal Kernel Orchestration for Tensor Programs with Korch

Astaroth: A Scalable Multi-GPU Library for Stencil Computations

Stencil Computations on AMD and Nvidia Graphics Processors: Performance and Tuning Strategies

Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs. Empirical tricks for LLM Jailbreaking

Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs

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high performance computing on graphics processing units: hgpu.org

Improving Performance of Iterative Applications through Interleaved Execution of Approximated CUDA Kernels

Reducing branch divergence to speed up parallel execution of unit testing on GPUs

Efficient GPU implementation of a class of array permutations

cuCatch: A Debugging Tool for Efficiently Catching Memory Safety Violations in CUDA Applications

EfficientBioAI: Making Bioimaging AI Models Efficient in Energy, Latency and Representation

GPUHarbor: Testing GPU Memory Consistency at Large

Program Analysis and Machine Learning based Approach to Predict Power Consumption of CUDA Kernel

Accelerating 128-bit Floating-Point Matrix Multiplication on FPGAs

Hybrid CPU/GPU/APU accelerated query, insert, update and erase operations in hash tables with string keys

Compiler Technologies in Deep Learning Co-Design: A Survey

Implementation Techniques for SPMD Kernels on CPUs

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

Recent source codes

GPUODEBenchmarks: Comparsion of Julia's GPU Kernel based ODE solvers with other open-source GPU ODE solvers

Dat3M: Memory Model Aware Verification

NVIDIA Federated Learning Application Runtime Environment

Chat AI: A Seamless Slurm-Native Solution for HPC-Based Services

PSCToolkit: solving sparse linear systems with a large number of GPUs

CATBench: Benchmarking Framework

General-purpose Polyhedral Compilers

Optimal Kernel Orchestration for Tensor Programs with Korch

Astaroth: A Scalable Multi-GPU Library for Stencil Computations

Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs. Empirical tricks for LLM Jailbreaking

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