{"id":45983,"url":"https://github.com/Jokeren/Awesome-GPU","name":"Awesome-GPU","description":"Awesome resources for GPUs","projects_count":85,"last_synced_at":"2026-07-13T22:00:19.848Z","repository":{"id":23328746,"uuid":"98725490","full_name":"Jokeren/Awesome-GPU","owner":"Jokeren","description":"Awesome resources for 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Generation"],"sub_categories":["Profilers","Resources Management","Parallelism","Cache","Memory","White Papers","BLAS","Stencils","Scans","Deep Learning","Benchmarking","Models","Simulators","Scheduling","Compilers","Programming Models","Profile Guided Optimization","Binaries"],"readme":"Awesome-GPU\n=================\n\n   * [Architecture](#architecture)\n      * [Resources Management](#resources-management)\n      * [Parallelism](#parallelism)\n      * [Cache](#cache)\n      * [Memory](#memory)\n      * [White Papers](#white-papers)\n   * [Algorithms](#algorithms)\n      * [BLAS](#blas)\n      * [Stencils](#stencils)\n      * [Scans](#scans)\n   * [Applications](#applications)\n      * [Deep Learning](#deep-learning)\n   * [Tools](#tools)\n      * [Benchmarks](#benchmarks)\n      * [Models](#models)\n      * [Simulators](#simulators)\n      * [Profilers](#profilers)\n   * [Runtime](#runtime)\n      * [Scheduling](#scheduling)\n   * [Code Generation](#code-generation)\n      * [Compilers](#compilers)\n      * [Programming Models](#programming-models)\n      * [Profile Guided Optimization](#profile-guided-optimization)\n      * [Binaries](#binaries)\n\n## Architecture\n\n### Resources Management\n\n- **TECS'21**-[Reducing Energy in GPGPUs through Approximate Trivial Bypassing](https://dl.acm.org/doi/10.1145/3429440)\n- **ASPLOS'17**-[Locality-Aware CTA Clustering for Modern GPUs](http://dl.acm.org/citation.cfm?id=3037709)\n- **ASPLOS'17**-[Dynamic Resource Management for Efficient Utilization of Multitasking GPUs](http://dl.acm.org/citation.cfm?id=3037707)\n- **HPCA'17**-[Dynamic GPGPU Power Management Using Adaptive Model Predictive Control](http://ieeexplore.ieee.org/document/7920860/)\n- **ISCA'16**-[Transparent Offloading and Mapping (TOM): Enabling Programmer-Transparent Near-Data Processing in GPU Systems](http://ieeexplore.ieee.org/document/7551394/)\n\n### Parallelism\n\n- **HPCA'18**-[Accelerate GPU Concurrent Kernel Execution by Mitigating Memory Pipeline Stalls](https://ieeexplore.ieee.org/abstract/document/8327010)\n- **HPCA'17**-[Controlled Kernel Launch for Dynamic Parallelism in GPUs](http://ieeexplore.ieee.org/document/7920863/)\n- **GTC'17**-[COOPERATIVE GROUPS](http://on-demand.gputechconf.com/gtc/2017/presentation/s7622-Kyrylo-perelygin-robust-and-scalable-cuda.pdf)\n- **ISCA'16**-[LaPerm: Locality Aware Scheduler for Dynamic Parallelism on GPUs](http://ieeexplore.ieee.org/document/7551424/)\n- **ISCA'16**-[Virtual Thread Maximizing Thread-Level Parallelism beyond GPU Scheduling Limit](http://ieeexplore.ieee.org/document/7551426/)\n- **Berkeley TechRpts'16**-[Understanding Latency Hiding on GPUs](https://www2.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-143.html)\n\n### Cache\n\n- **ISCA'16**-[APRES: Improving Cache Efficiency by Exploiting Load Characteristics on GPUs](http://ieeexplore.ieee.org/document/7551393/)\n- **SC'15**-[Adaptive and Transparent Cache Bypassing for GPUs](https://ieeexplore.ieee.org/document/7832791)\n\n### Memory\n\n- **ICCAD'21**-[Improving Inter-kernel Data Reuse With CTA-Page Coordination in GPGPU](https://ieeexplore.ieee.org/document/9643535)\n- **SC'21**-[In-Depth Analyses of Unified Virtual Memory System for GPU Accelerated Computing](https://dl.acm.org/doi/10.1145/3458817.3480855)\n- **IBM'20**-[Umpire: Application-Focused Management and Coordination of Complex Hierarchical Memory](https://ieeexplore.ieee.org/document/8907404)\n- **HPCA'13**-[Reducing GPU Offload Latency via Fine-Grained CPU-GPU Synchronization](https://ieeexplore.ieee.org/document/6522332)\n\n### White Papers\n\n- **NVIDIA Hopper**-[NVIDIA H100 Tensor Core GPU Architecture](https://resources.nvidia.com/en-us-tensor-core)\n- **NVIDIA Ampere**-[NVIDIA A100 Tensor Core GPU Architecture](https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Center/nvidia-ampere-architecture-whitepaper.pdf)\n- **NVIDIA Turing**-[NVIDIA TURING GPU ARCHITECTURE](https://www.nvidia.com/en-us/design-visualization/technologies/turing-architecture/)\n- **NVIDIA Volta**-[NVIDIA TESLA V100](http://www.nvidia.com/object/volta-architecture-whitepaper.html)\n- **NVIDIA Pascal**-[NVIDIA TESLA P100](http://www.nvidia.com/object/gpu-architecture.html)\n- **NVIDIA Kepler**-[NVIDIA’s Next Generation CUDA Compute Architecture: Kepler](https://www.nvidia.com/content/PDF/kepler/NVIDIA-Kepler-GK110-Architecture-Whitepaper.pdf)\n- **NVIDIA Fermi**-[NVIDIA’s Next Generation CUDA Compute Architecture: Fermi](https://www.nvidia.com/content/PDF/fermi_white_papers/NVIDIA_Fermi_Compute_Architecture_Whitepaper.pdf)\n- **AMD CDNA 2**-[INTRODUCING AMD CDNA 2 ARCHITECTURE](https://www.amd.com/system/files/documents/amd-cdna2-white-paper.pdf)\n- **AMD CDNA**-[INTRODUCING AMD CDNA ARCHITECTURE](https://www.amd.com/system/files/documents/amd-cdna-whitepaper.pdf)\n\n## Algorithms\n\n### BLAS\n\n- **GTC'20**-[DEVELOPING CUDA KERNELS TO PUSH TENSOR CORES TO THE ABSOLUTE LIMIT ON NVIDIA A100](https://developer.download.nvidia.com/video/gputechconf/gtc/2020/presentations/s21745-developing-cuda-kernels-to-push-tensor-cores-to-the-absolute-limit-on-nvidia-a100.pdf)\n- **IPDPS'20**-[Demystifying Tensor Cores to Optimize Half-Precision Matrix Multiply](https://ieeexplore.ieee.org/abstract/document/9139835)\n- **PPoPP'19**-[A Coordinated Tiling and Batching Framework for Efficient GEMM on GPU](https://dl.acm.org/doi/10.1145/3293883.3295734)\n- **GTC'18**-[CUTLASS: CUDA TEMPLATE LIBRARY FOR DENSE LINEAR ALGEBRA AT ALL LEVELS AND SCALES](http://on-demand.gputechconf.com/gtc/2018/presentation/s8854-cutlass-software-primitives-for-dense-linear-algebra-at-all-levels-and-scales-within-cuda.pdf)\n\n### Stencils\n\n- **CGO'20**-[AN5D: Automated Stencil Framework for High-Degree Temporal Blocking on GPUs](https://dl.acm.org/doi/10.1145/3368826.3377904)\n- **IPDPS'20**-[On Optimizing Complex Stencils on GPUs](https://ieeexplore.ieee.org/document/8820786)\n- **PPoPP'18**-[Register Optimizations for Stencils on GPUs](https://dl.acm.org/doi/abs/10.1145/3178487.3178500)\n\n### Scans\n\n- **NVResearch TechRpts'16**-[Single-pass Parallel Prefix Scan with Decoupled Look-back](https://research.nvidia.com/publication/single-pass-parallel-prefix-scan-decoupled-look-back)\n\n## Applications\n\n### Deep Learning\n\n- **PPoPP'21**-[Understanding and bridging the gaps in current GNN performance optimizations](https://dl.acm.org/doi/10.1145/3437801.3441585)\n- **SC'21**-[E.T.: re-thinking self-attention for transformer models on GPUs](https://dl.acm.org/doi/abs/10.1145/3458817.3476138)\n- **OSDI'21**-[GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs](https://www.usenix.org/system/files/osdi21-wang-yuke.pdf)\n- **SC'20**-[Sparse GPU Kernels for Deep Learning](https://arxiv.org/abs/2006.10901)\n- **PPoPP'18**-[SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks](https://arxiv.org/abs/1801.04380)\n- **HPCA'17**-[Towards Pervasive and User Satisfactory CNN across GPU Microarchitectures](http://ieeexplore.ieee.org/document/7920809/)\n\n## Tools\n\n### Benchmarking\n\n- **GTC'18**-[Dissecting the NVIDIA Volta GPU Architecture via Microbenchmarking](https://arxiv.org/pdf/1804.06826.pdf)\n- **ISPASS'10**-[Demystifying GPU Microarchitecture through Microbenchmarking](http://ieeexplore.ieee.org/document/5452013/)\n\n### Models\n\n- **PMBS'19**-[Instruction Roofline An insightful visual performance model for GPUs](https://ieeexplore.ieee.org/document/9059264)\n- **ECP'19**-[Performance Tuning of Scientific Codes with the Roofline Model](https://crd.lbl.gov/assets/Uploads/ECP19-Roofline-1-intro.pdf)\n- **GTC'18**-[VOLTA Architecture and performance optimization](http://on-demand.gputechconf.com/gtc/2018/presentation/s81006-volta-architecture-and-performance-optimization.pdf)\n- **Synthesis Lectures on Computer Architecture'12**-[Performance Analysis and Tuning for General Purpose Graphics Processing Units (GPGPU)](http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6812836\u0026newsearch=true\u0026queryText=Performance%20Analysis%20and%20Tuning%20for%20General%20Purpose%20Graphics%20Processing%20Units%2038%20.LB.GPGPU.RB.)\n- **SC'10**-[Fundamental_Optimizations](https://www.nvidia.com/content/PDF/sc_2010/CUDA_Tutorial/SC10_Fundamental_Optimizations.pdf)\n\n### Simulators\n\n- **ISPASS'10**-[Visualizing Complex Dynamics in Many-Core Accelerator Architectures](http://ieeexplore.ieee.org/document/5452029/)\n- **ISPASS'09**-[Analyzing CUDA Workloads Using a Detailed GPU Simulator](http://ieeexplore.ieee.org/abstract/document/4919648/)\n\n### Profilers\n\n- **PLDI'18**-[GPU Code Optimization using Abstract Kernel Emulation and Sensitivity Analysis](https://dl.acm.org/citation.cfm?id=3192397)\n- **CGO'18**-[CUDAAdvisor: LLVM-based runtime profiling for modern GPUs](https://dl.acm.org/citation.cfm?id=3168831)\n- **CCGRID'18**-[Exposing Hidden Performance Opportunities in High Performance GPU Applications ](https://ieeexplore.ieee.org/document/8411034)\n- **THPC'16**-[Monitoring Heterogeneous Applications with the OpenMP Tools Interface](https://link.springer.com/chapter/10.1007/978-3-319-56702-0_3)\n- **Euro-Par'15**-[Identifying Optimization Opportunities Within Kernel Execution in GPU Codes](https://link.springer.com/chapter/10.1007/978-3-319-27308-2_16)\n- **SC'13**-[Effective sampling-driven performance tools for GPU-accelerated supercomputers](https://dl.acm.org/citation.cfm?id=2503299)\n- **ISPASS'12**-[Lynx: A dynamic instrumentation system for data-parallel applications on GPGPU architectures ](https://ieeexplore.ieee.org/document/6189206)\n- **ICPP'11**-[Parallel Performance Measurement of Heterogeneous Parallel Systems with GPUs](https://dl.acm.org/citation.cfm?id=2066951)\n- [**Vampir|Score-P**](http://www.vi-hps.org/projects/score-p/)\n- [**TAU**](https://www.cs.uoregon.edu/research/tau/home.php)\n- [**PAPI**](http://icl.utk.edu/papi/)\n- [**Allinea MAP**](https://www.allinea.com/products/map/)\n- [**Open|SpeedShop**](https://openspeedshop.org/)\n- [**HPCToolkit**](http://hpctoolkit.org/)\n- [**Ingero**](https://github.com/ingero-io/ingero) - eBPF-based GPU causal observability agent. Traces CUDA Runtime/Driver APIs via uprobes and host kernel events to build causal chains explaining GPU latency. \u003c2% overhead, production-safe.\n- [**NVIDIA Nsight Systems**](https://developer.nvidia.com/nsight-systems)\n- [**NVIDIA Nsight Compute**](https://developer.nvidia.com/nsight-compute)\n- [**SASSI**](https://github.com/NVlabs/SASSI/blob/master/doc/SASSI-Tutorial-Micro2015.pptx)\n- [**NVBit**](https://github.com/NVlabs/NVBit/releases)\n\n## Runtime\n\n### Scheduling\n\n- **PPoPP'22**-[CASE: A Compiler-Assisted SchEduling Framework for Multi-GPU Systems](https://arxiv.org/abs/2107.08538)\n- **TPDS'20**-[cCUDA: Effective Co-Scheduling of Concurrent Kernels on GPUs](https://www.computer.org/csdl/journal/td/2020/04/08853389/1dKnnndWFwY)\n\n## Code Generation\n\n### Compilers\n\n- **AMD'21**-[Generating GPU Compiler Heuristics using Reinforcement Learning](https://arxiv.org/abs/2111.12055)\n- **TACO'21**-[Domain-Specific Multi-Level IR Rewriting for GPU: The Open Earth Compiler for GPU-accelerated Climate Simulation](https://dl.acm.org/doi/10.1145/3469030)\n- **LLVM'17**-[Implementing implicit OpenMP data sharing on GPUs](https://dl.acm.org/citation.cfm?id=3148189)\n- **CGO'16**-[gpucc: An Open-Source GPGPU Compiler](http://dl.acm.org/citation.cfm?id=2854041)\n- **LLVM'16**-[Offloading Support for OpenMP in Clang and LLVM](https://dl.acm.org/citation.cfm?id=3018870)\n- **PMBS'15**-[Performance Analysis of OpenMP on a GPU using a CORAL Proxy Application](https://dl.acm.org/citation.cfm?id=2832089)\n- **LLVM'15**-[Integrating GPU Support for OpenMP Offloading Directives into Clang](https://dl.acm.org/citation.cfm?id=2833161)\n- **LLVM'14**-[Coordinating GPU Threads for OpenMP 4.0 in LLVM](https://dl.acm.org/citation.cfm?id=2688364)\n\n### Programming Models\n\n- **CGO'21**-[C-for-metal: high performance SIMD programming on intel GPUs](https://dl.acm.org/doi/abs/10.1109/CGO51591.2021.9370324)\n- **ECRTS'19**-[Novel Methodologies for Predictable CPU-To-GPU Command Offloading](https://drops.dagstuhl.de/opus/volltexte/2019/10759/)\n- **ASPLOS'14**-[Paraprox: Pattern-Based Approximation for Data Parallel Applications](https://dl.acm.org/citation.cfm?id=2541948)\n\n### Profile Guided Optimization\n\n- **Geometry and Optimization'21**-[Cooperative Profile Guided Optimizations](https://doi.org/10.1111/cgf.14382)\n- **IPDPS'13**-[Kernel Specialization for Improved Adaptability and Performance on Graphics Processing Units (GPUs)](https://ieeexplore.ieee.org/document/6569883)\n\n### Binaries\n\n- **CGO'19**-[Decoding CUDA binary](https://dl.acm.org/citation.cfm?id=3314900)\n- **ISCA'15**-[Flexible software profiling of GPU architectures](http://ieeexplore.ieee.org/document/7284065/)\n\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/jokeren%2Fawesome-gpu/projects"}