high performance computing on graphics processing units: hgpu.org

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Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

Jacob Wahlgren, Gabin Schieffer, Ruimin Shi, Edgar A. León, Roger Pearce, Maya Gokhale, Ivy Peng

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Tags: AMD Radeon Instinct MI300A, ATI, Benchmarking, Computer science, HIP, Memory model, Package

August 31, 2025 by hgpu

Dynamic Memory Management on GPUs with SYCL

Russell K. Standish

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Tags: Computer science, CUDA, HIP, Memory model, nVidia, Package, Performance, SYCL

May 4, 2025 by hgpu

TransCL: An Automatic CUDA-to-OpenCL Programs Transformation Framework

Changqing Shi, Yufei Sun, Rui Chen, Jiahao Wang, Qiang Guo, Chunye Gong, Yicheng Sui, Yutong Jin, Yuzhi Zhang

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Tags: Code generation, Computer science, CUDA, HPC, Memory model, nVidia, OpenCL, Package

March 3, 2025 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

Managing heterogeneous device memory using C++17 memory resources

S.N. Swatman, A. Krasznahorkay, P. Gessinger

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Tags: Computer science, CUDA, Heterogeneous systems, HIP, Memory model, nVidia, Package, SYCL

April 2, 2023 by hgpu

Comparing SYCL data transfer strategies for tracking use cases

S.Joube, H. Grasland, D. Chamont, E.Brunet

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Tags: Benchmarking, Computer science, Memory model, nVidia, nVidia Quadro RTX 5000, SYCL

March 26, 2023 by hgpu

Towards Understanding and Mitigating Memory-Access Challenges in Computing Systems

Mohammad K. Dashti

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Memory model, nVidia, nVidia GeForce RTX 2070, nVidia Jetson TK1, OpenCL, Package, Thesis

August 28, 2022 by hgpu

Optimizing Communication for Clusters of GPUs

Michael Wayne LeBeane

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Tags: Computer science, GPU cluster, Memory model, Thesis

September 2, 2018 by hgpu

Analyzing Memory Accesses for Performance and Correctness of Parallel Programs

Tim Cramer

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Tags: Computer science, Heterogeneous systems, HPC, Intel Xeon Phi, Memory model, OpenMP, Parallel programming, Thesis

June 28, 2018 by hgpu

Implementing implicit OpenMP data sharing on GPUs

Gheorghe-Teodor Bercea, Carlo Bertolli, Arpith C. Jacob, Alexandre Eichenberger, Alexey Bataev, Georgios Rokos, Hyojin Sung, Tong Chen, Kevin O'Brien

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Tags: Computer science, CUDA, LLVM, Memory model, nVidia, OpenMP, Programming Languages, PTX, Tesla K40, Tesla P100

November 30, 2017 by hgpu

Improving the Neural GPU Architecture for Algorithm Learning

Karlis Freivalds, Renars Liepins

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Tags: Algorithms, Artificial intelligence, Computer science, Deep learning, Memory model, nVidia, Package, Python, TensorFlow, Tesla K40

March 5, 2017 by hgpu

OpenCL + OpenSHMEM Hybrid Programming Model for the Adapteva Epiphany Architecture

David Richie, James Ross

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Tags: Algorithms, ARM, Computer science, Matrix multiplication, Memory model, OpenCL, Package

August 16, 2016 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

Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

Dynamic Memory Management on GPUs with SYCL

TransCL: An Automatic CUDA-to-OpenCL Programs Transformation Framework

GPUHarbor: Testing GPU Memory Consistency at Large

Managing heterogeneous device memory using C++17 memory resources

Comparing SYCL data transfer strategies for tracking use cases

Towards Understanding and Mitigating Memory-Access Challenges in Computing Systems

Optimizing Communication for Clusters of GPUs

Analyzing Memory Accesses for Performance and Correctness of Parallel Programs

Implementing implicit OpenMP data sharing on GPUs

Improving the Neural GPU Architecture for Algorithm Learning

OpenCL + OpenSHMEM Hybrid Programming Model for the Adapteva Epiphany Architecture

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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