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A comparison of HPC-based quantum computing simulators using Quantum Volume

Lourens van Niekerk, Dhiraj Kumar, Aasish Kumar Sharma, Tino Meisel, Martin Leandro Paleico, Christian Boehme

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Tags: Benchmarking, CUDA, nVidia, nVidia A100, OpenCL, Overview, Physics, Quantum computing, Review

January 6, 2025 by hgpu

Harnessing Integrated CPU-GPU System Memory for HPC: a first look into Grace Hopper

Gabin Schieffer, Jacob Wahlgren, Jie Ren, Jennifer Faj, Ivy Peng

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Tags: Computer science, CUDA, HPC, Memory, nVidia, nVidia H100, Performance, Quantum computing

July 14, 2024 by hgpu

From GPUs to AI and quantum: three waves of acceleration in bioinformatics

Bertil Schmidt, Andreas Hildebrandt

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Tags: Artificial intelligence, Bioinformatics, Biology, Chemistry, Neural networks, Quantum computing

May 20, 2024 by hgpu

Enabling Quantum Computer Simulations on AMD GPUs: a HIP Backend for Google’s qsim

Stefano Markidis

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Tags: AMD Radeon Instinct MI250X, Computer science, CUDA, HIP, nVidia, nVidia A100, Quantum computing

December 31, 2023 by hgpu

When Machine Learning Meets Quantum Computers: A Case Study

Weiwen Jiang, Jinjun Xiong, Yiyu Shi

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Tags: Computer science, CUDA, FPGA, Machine learning, Neural networks, nVidia, Quantum computing

December 27, 2020 by hgpu

Simulating Quantum Computers Using OpenCL

Adam Kelly

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Source codes

Tags: Computer science, nVidia, OpenCL, Package, Quantum computing, Quantum Physics, Tesla V100

May 5, 2018 by hgpu

A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers

Thomas E. Potok, Catherine Schuman, Steven R. Young, Robert M. Patton, Federico Spedalieri, Jeremy Liu, Ke-Thia Yao, Garrett Rose, Gangotree Chakma

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Tags: CNN, Computer science, Deep learning, Neural networks, Quantum computing

March 20, 2017 by hgpu

GPU-accelerated algorithms for many-particle continuous-time quantum walks

Enrico Piccinini, Claudia Benedetti, Ilaria Siloi, Matteo G. A. Paris, Paolo Bordone

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Tags: Algorithms, Benchmarking, Computational Physics, CUDA, nVidia, nVidia GeForce GTX 980, OpenMP, Physics, Quantum computing, Quantum Physics, Quantum walks, Tesla K40, Tesla K80, Tesla M2050

December 6, 2016 by hgpu

Full reconstruction of a 14-qubit state within four hours

Zhibo Hou, Han-Sen Zhong, Ye Tian, Daoyi Dong, Bo Qi, Li Li, Yuanlong Wang, Franco Nori, Guo-Yong Xiang, Chuan-Feng Li, Guang-Can Guo

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Tags: CUDA, nVidia, nVidia GeForce GTX 780, Physics, Quantum computing, Quantum Physics

March 3, 2016 by hgpu

Assessing the hardness of SVP algorithms in the presence of CPUs and GPUs

Fabio Jose Goncalves Correia

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Tags: Algorithms, Computer science, CUDA, MPI, nVidia, Quantum computing, Security, Thesis, Voronoi diagram

September 8, 2015 by hgpu

Accelerating NTRU Encryption with Graphics Processing Units

Tianyu Bai, Spencer Davis, Juanjuan Li, Ying Gu, Hai Jiang

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 680, Quantum computing, Security

October 6, 2014 by hgpu

Expanding the VPE-qGM Environment Towards a Parallel Quantum Simulation of Quantum Processes Using GPUs

Adriano Maron, Renata Reiser, Mauricio Pilla, Adenauer Yamin

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GT 640, Quantum computing

February 28, 2014 by hgpu

Code Generation for Cryptographic Kernels using Multi-word Modular Arithmetic on GPU

GSParLib

GSParLib: A multi-level programming interface unifying OpenCL and CUDA for expressing stream and data parallelism

Keras Sig: Efficient Path Signature Computation on GPU in Keras 3

KSig: GPU-accelerated computation of popular time series kernels

A User’s Guide to KSig: GPU-Accelerated Computation of the Signature Kernel

IterApp: Iterative Application Skeleton

Boosting Performance of Iterative Applications on GPUs: Kernel Batching with CUDA Graphs

SCALE-Ahead-Of-Time Compilation of CUDA for AMD GPUs

LeetDecoding: A PyTorch Library for Exponentially Decaying Causal Linear Attention with CUDA Implementations

Anari-visionaray

Data Parallel Visualization and Rendering on the RAMSES Supercomputer with ANARI

PROM: an open-source software framework to enhance the robustness and performance of predictive models against such changes during deployment

Enhancing Deployment-Time Predictive Model Robustness for Code Analysis and Optimization

DeepSpeed: a deep learning optimization library that makes distributed training and inference easy, efficient, and effective

Scalable Access-Pattern Aware I/O Acceleration and Multi-Tiered Data Management for HPC and AI Workloads

See all packages

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high performance computing on graphics processing units: hgpu.org

A comparison of HPC-based quantum computing simulators using Quantum Volume

Harnessing Integrated CPU-GPU System Memory for HPC: a first look into Grace Hopper

From GPUs to AI and quantum: three waves of acceleration in bioinformatics

Enabling Quantum Computer Simulations on AMD GPUs: a HIP Backend for Google’s qsim

When Machine Learning Meets Quantum Computers: A Case Study

Simulating Quantum Computers Using OpenCL

A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers

GPU-accelerated algorithms for many-particle continuous-time quantum walks

Full reconstruction of a 14-qubit state within four hours

Assessing the hardness of SVP algorithms in the presence of CPUs and GPUs

Accelerating NTRU Encryption with Graphics Processing Units

Expanding the VPE-qGM Environment Towards a Parallel Quantum Simulation of Quantum Processes Using GPUs

Recent source codes

Code Generation for Cryptographic Kernels using Multi-word Modular Arithmetic on GPU

GSParLib

Keras Sig: Efficient Path Signature Computation on GPU in Keras 3

KSig: GPU-accelerated computation of popular time series kernels

IterApp: Iterative Application Skeleton

SCALE-Ahead-Of-Time Compilation of CUDA for AMD GPUs

LeetDecoding: A PyTorch Library for Exponentially Decaying Causal Linear Attention with CUDA Implementations

Anari-visionaray

PROM: an open-source software framework to enhance the robustness and performance of predictive models against such changes during deployment

DeepSpeed: a deep learning optimization library that makes distributed training and inference easy, efficient, and effective

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