high performance computing on graphics processing units: hgpu.org

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High-Performance GPU-to-CPU Transpilation and Optimization via High-Level Parallel Constructs

William S. Moses, Ivan R. Ivanov, Jens Domke, Toshio Endo, Johannes Doerfert, Oleksandr Zinenko

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce RTX 2080 Ti, Package, performance portability, Programming Languages

July 10, 2022 by hgpu

DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardware

Hanieh Hashemi, Yongqin Wang, Murali Annavaram

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Tags: Algorithms, Computer science, Deep learning, Linear Algebra, Machine learning, nVidia, nVidia GeForce GTX 1080 Ti, Security

July 10, 2022 by hgpu

Novel Parallel Approaches to Efficiently Solve Spatial Problems on Heterogeneous CPU-GPU Systems

Andrés Jesús Sánchez Fernández

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Tags: Algorithms, Computer science, Databases, Heterogeneous systems, nVidia, nVidia GeForce GT 1030, nVidia GeForce GTX 1050 Ti, nVidia GeForce GTX 980, Thesis

July 3, 2022 by hgpu

Evaluation of Intel’s DPC++ Compatibility Tool in heterogeneous computing

Germán Castaño, Youssef Faqir-Rhazoui, Carlos García, Manuel Prieto-Matías

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, Package, Performance, SYCL

July 3, 2022 by hgpu

Tensor Computation Based on Heterogeneous Memory

Jiawen Liu

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Tags: Computer science, Heterogeneous systems, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, OpenCL, Prefetch, Thesis

July 3, 2022 by hgpu

Optimizing the Performance of Parallel and Concurrent Applications Based on Asynchronous Many-Task Runtimes

Weile Wei

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, Optimization, Package, QMC, Tesla V100, Thesis

July 3, 2022 by hgpu

TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s

Felix Chern, Blake Hechtman, Andy Davis, Ruiqi Guo, David Majnemer, Sanjiv Kumar

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Tags: Algorithms, Computer science, Nearest neighbour, nVidia, nVidia A100, Tesla V100, TPU

July 3, 2022 by hgpu

SnuHPL: high performance LINPACK for heterogeneous GPUs

Jinpyo Kim, Hyungdal Kwon, Jintaek Kang, Jihwan Park, Seungwook Lee, Jaejin Lee

Tags: Benchmarking, Computer science, nVidia, nVidia A100

June 26, 2022 by hgpu

tntorch: Tensor Network Learning with PyTorch

Mikhail Usvyatsov, Rafael Ballester-Ripoll, Konrad Schindler

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Tags: Computer science, Deep learning, nVidia, nVidia GeForce RTX 3090, Package, Python, PyTorch

June 26, 2022 by hgpu

MapReduce for Counting Word Frequencies with MPI and GPUs

Nithin Kavi

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Tags: Algorithms, Computer science, CUDA, Julia, MapReduce, MPI, NLP, nVidia, Optimization, Package, Tesla V100

June 19, 2022 by hgpu

PILC: Practical Image Lossless Compression with an End-to-end GPU Oriented Neural Framework

Ning Kang, Shanzhao Qiu, Shifeng Zhang, Zhenguo Li, Shutao Xia

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Tags: Algorithms, Compression, Image processing, nVidia, Tesla V100

June 19, 2022 by hgpu

Securing GPU via Region-based Bounds Checking

Jaewon Lee, Yonghae Kim, Jiashen Cao, Euna Kim, Jaekyu Lee, Hyesoon Kim

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce RTX 2080, OpenCL, Security

June 19, 2022 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

High-Performance GPU-to-CPU Transpilation and Optimization via High-Level Parallel Constructs

DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardware

Novel Parallel Approaches to Efficiently Solve Spatial Problems on Heterogeneous CPU-GPU Systems

Evaluation of Intel’s DPC++ Compatibility Tool in heterogeneous computing

Tensor Computation Based on Heterogeneous Memory

Optimizing the Performance of Parallel and Concurrent Applications Based on Asynchronous Many-Task Runtimes

TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s

SnuHPL: high performance LINPACK for heterogeneous GPUs

tntorch: Tensor Network Learning with PyTorch

MapReduce for Counting Word Frequencies with MPI and GPUs

PILC: Practical Image Lossless Compression with an End-to-end GPU Oriented Neural Framework

Securing GPU via Region-based Bounds Checking

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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