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GCStack+GCScaler: Fast and Accurate GPU Performance Analyses Using Fine-Grained Stall Cycle Accounting and Interval Analysis

Hanna Cha, Sungchul Lee, Jounghoo Lee, Yeonan Ha, Joonsung Kim, Youngsok Kim

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Tags: Benchmarking, Computer science, CUDA, Design space exploration, nVidia, nVidia GeForce RTX 3070, Performance

June 29, 2025 by hgpu

Analyzing the Performance Portability of SYCL across CPUs, GPUs, and Hybrid Systems with Protein Database Search

Manuel Costanzo, Enzo Rucci, Carlos García-Sánchez, Marcelo Naiouf, Manuel Prieto-Matías

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Tags: AMD Radeon RX 6700 XT, AMD Radeon RX Vega 6, ATI, Bioinformatics, Biology, Computer science, CUDA, Databases, Heterogeneous systems, HPC, Intel, Intel Arc A770, Intel UHD 630, Intel UHD 770, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980, nVidia GeForce RTX 2070, nVidia GeForce RTX 3070, nVidia GeForce RTX 3090, oneAPI, Package, performance portability, SYCL, Tesla V100

December 15, 2024 by hgpu

Green AI: A Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures

Negar Alizadeh, Fernando Castor

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Tags: Computer science, CUDA, Deep learning, Energy-efficient computing, nVidia, nVidia GeForce RTX 3070, Package, PyTorch, TensorFlow

February 25, 2024 by hgpu

Comparing Performance and Portability between CUDA and SYCL for Protein Database Search on NVIDIA, AMD, and Intel GPUs

Manuel Costanzo, Enzo Rucci, Carlos García Sánchez, Marcelo Naiouf, Manuel Prieto-Matías

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Tags: AMD Radeon RX 6700 XT, ATI, Bioinformatics, Biology, Computer science, CUDA, Intel, Intel UHD 630, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980, nVidia GeForce RTX 2070, nVidia GeForce RTX 3070, nVidia GeForce RTX 3090, oneAPI, Programming Languages, Smith-Waterman algorithm, SYCL, Tesla V100

September 24, 2023 by hgpu

Unified Shared Memory: Friend or Foe?

Juan Fumero Alfonso, Florin-Gabriel Blanaru, Athanasios Stratikopoulos, Steve Dohrmann, Sandhya Viswanathan, Christos-Efthymios Kotselidis

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Tags: Code generation, Computer science, CUDA, FPGA, Heterogeneous systems, Java, Memory, nVidia, nVidia GeForce RTX 3070, OpenCL

September 17, 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

ACRoBat: Optimizing Auto-batching of Dynamic Deep Learning at Compile Time

Pratik Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry

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Tags: Code generation, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce RTX 3070

May 28, 2023 by hgpu

Improving the scalability of modern applications by parallel multi-core and many-core programming

Alessandro Borione

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, nVidia GeForce RTX 3070, OpenCL, Thesis

January 15, 2023 by hgpu

GPU-accelerated Faster Mean Shift with euclidean distance metrics

Le You, Han Jiang, Jinyong Hu, Chorng Chang, Lingxi Chen, Xintong Cui, Mengyang Zhao

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Tags: Algorithms, Clustering, CUDA, Image processing, nVidia, nVidia GeForce RTX 3070, nVidia GeForce RTX 3080 Ti, nVidia Quadro P 5000, Package, Pattern recognition, Tesla P100

January 2, 2022 by hgpu

Specx: Speculative task-based runtime system

Specx: a C++ task-based runtime system for heterogeneous distributed architectures

Mutual-Supervised Learning for Sequential-to-Parallel Code Translation

KISim: Kubernetes Intelligent Scheduling Simulator

KIS-S: A GPU-Aware Kubernetes Inference Simulator with RL-Based Auto-Scaling

Hardware Compute Partitioning on NVIDIA GPUs for Composable Systems

Efficient GPU Implementation of Multi-Precision Integer Division

ParEval: A Parallel Code Evaluation Benchmark

ParEval-Repo: A Benchmark Suite for Evaluating LLMs with Repository-level HPC Translation Tasks

FlashSparse: Minimizing Computation Redundancy for Fast Sparse Matrix Multiplications on Tensor Cores

Libra: Synergizing CUDA and Tensor Cores for High-Performance Sparse Matrix Multiplication

exa-AMD: Exascale Accelerated Materials Discovery

Accelerated discovery and design of Fe-Co-Zr magnets with tunable magnetic anisotropy through machine learning and parallel computing

WiLLM: An Open Wireless LLM Communication System

Vcc: the Vulkan Clang Compiler

No More Shading Languages: Compiling C++ to Vulkan Shaders

See all packages

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high performance computing on graphics processing units: hgpu.org

GCStack+GCScaler: Fast and Accurate GPU Performance Analyses Using Fine-Grained Stall Cycle Accounting and Interval Analysis

Analyzing the Performance Portability of SYCL across CPUs, GPUs, and Hybrid Systems with Protein Database Search

Green AI: A Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures

Comparing Performance and Portability between CUDA and SYCL for Protein Database Search on NVIDIA, AMD, and Intel GPUs

Unified Shared Memory: Friend or Foe?

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

ACRoBat: Optimizing Auto-batching of Dynamic Deep Learning at Compile Time

Improving the scalability of modern applications by parallel multi-core and many-core programming

GPU-accelerated Faster Mean Shift with euclidean distance metrics

Recent source codes

Specx: Speculative task-based runtime system

Mutual-Supervised Learning for Sequential-to-Parallel Code Translation

KISim: Kubernetes Intelligent Scheduling Simulator

Hardware Compute Partitioning on NVIDIA GPUs for Composable Systems

Efficient GPU Implementation of Multi-Precision Integer Division

ParEval: A Parallel Code Evaluation Benchmark

FlashSparse: Minimizing Computation Redundancy for Fast Sparse Matrix Multiplications on Tensor Cores

exa-AMD: Exascale Accelerated Materials Discovery

WiLLM: An Open Wireless LLM Communication System

Vcc: the Vulkan Clang Compiler

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