Mass matrix assembly for implicit PIC methods can be exactly reformulated cell-by-cell as tensor-core matrix products, delivering up to 3x kernel speedup and 15% end-to-end runtime reduction in ECSIM simulations.
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KerneLDI accelerates exchange-correlation integration in Kohn-Sham DFT by up to 10x through block-structured matrix multiplication that exploits spatial locality on GPUs while preserving accuracy.
A fused gather-GEMM-scatter CUDA kernel achieves 4.6-7.3x end-to-end speedup and 3.2-4.9x lower energy for matrix-free 3D SIMP topology optimization on RTX 4090 compared to three-stage baselines.
The paper introduces matrix-multiplication-based iterative refinement for diagonalizable non-Hermitian eigendecompositions that achieves quadratic residual reduction for simple eigenvalues and includes cluster stabilization.
Develops mixed-precision iterative refinement for low-rank Lyapunov equations with rounding error analysis enabling reduced precision for moderately conditioned problems.
Duon eliminates TLB shootdown and cache invalidation costs during page migration in flat-address hybrid memory systems by updating mappings in-place, delivering 3.87% IPC gains over prior methods.
Aurora reached 1.01 EF/s FP64 HPL and 11.64 EF/s HPL-MxP through locality-aware mapping, CPU-GPU pipelining, mixed-precision orchestration, and hybrid resilience on a large Intel GPU-based system.
A literature review that categorizes deep learning approaches for visual hand gesture recognition, summarizes state-of-the-art methods across tasks, reviews datasets and metrics, and identifies challenges and future directions.
citing papers explorer
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Mass Matrix Assembly on Tensor Cores for Implicit Particle-In-Cell Methods
Mass matrix assembly for implicit PIC methods can be exactly reformulated cell-by-cell as tensor-core matrix products, delivering up to 3x kernel speedup and 15% end-to-end runtime reduction in ECSIM simulations.
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Accelerating Locality-Driven Integration in Quantum Chemistry with Block-Structured Matrix Multiplication
KerneLDI accelerates exchange-correlation integration in Kohn-Sham DFT by up to 10x through block-structured matrix multiplication that exploits spatial locality on GPUs while preserving accuracy.
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Matrix-Free 3D SIMP Topology Optimization with Fused Gather-GEMM-Scatter Kernels
A fused gather-GEMM-scatter CUDA kernel achieves 4.6-7.3x end-to-end speedup and 3.2-4.9x lower energy for matrix-free 3D SIMP topology optimization on RTX 4090 compared to three-stage baselines.
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Iterative Refinement for Diagonalizable Non-Hermitian Eigendecompositions
The paper introduces matrix-multiplication-based iterative refinement for diagonalizable non-Hermitian eigendecompositions that achieves quadratic residual reduction for simple eigenvalues and includes cluster stabilization.
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Mixed-precision iterative refinement for low-rank Lyapunov equations
Develops mixed-precision iterative refinement for low-rank Lyapunov equations with rounding error analysis enabling reduced precision for moderately conditioned problems.
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Efficient Page Migration in Hybrid Memory Systems
Duon eliminates TLB shootdown and cache invalidation costs during page migration in flat-address hybrid memory systems by updating mappings in-place, delivering 3.87% IPC gains over prior methods.
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Sustaining Exascale Performance: Lessons from HPL and HPL-MxP on Aurora
Aurora reached 1.01 EF/s FP64 HPL and 11.64 EF/s HPL-MxP through locality-aware mapping, CPU-GPU pipelining, mixed-precision orchestration, and hybrid resilience on a large Intel GPU-based system.
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Visual Hand Gesture Recognition with Deep Learning: A Comprehensive Review of Methods, Datasets, Challenges and Future Research Directions
A literature review that categorizes deep learning approaches for visual hand gesture recognition, summarizes state-of-the-art methods across tasks, reviews datasets and metrics, and identifies challenges and future directions.