AsyncSparse presents BCSR and WCSR kernels that use TMA and warp specialization to accelerate SpMM, outperforming prior libraries by 1.47-6.24x on SuiteSparse and achieving 2.66x end-to-end speedup on Qwen2.5-7B at 90% block sparsity.
Dissecting the nvidia hopper architecture through microbenchmarking and multiple level analysis
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 3
citation-polarity summary
years
2026 4verdicts
UNVERDICTED 4roles
background 3polarities
background 3representative citing papers
TLX introduces MIMW-based extensions to Triton that let developers orchestrate warp-group execution and asynchronous hardware features while preserving blocked programming productivity, with kernels deployed in large-scale training and inference.
Sim-FA is a new simulator that instruments FlashAttention-3 for cycle-accurate GPGPU analysis, achieving 5.7% average error on H800 while explaining inaccuracies in existing DRAM traffic models.
Microbenchmark-driven analytical models for B200 and MI300A achieve 1.31% and 0.09% MAE on validation kernels, far outperforming roofline baselines exceeding 95% error.
citing papers explorer
-
AsyncSparse: Accelerating Sparse Matrix-Matrix Multiplication on Asynchronous GPU Architectures
AsyncSparse presents BCSR and WCSR kernels that use TMA and warp specialization to accelerate SpMM, outperforming prior libraries by 1.47-6.24x on SuiteSparse and achieving 2.66x end-to-end speedup on Qwen2.5-7B at 90% block sparsity.
-
TLX: Hardware-Native, Evolvable MIMW GPU Compiler for Large-scale Production Environments
TLX introduces MIMW-based extensions to Triton that let developers orchestrate warp-group execution and asynchronous hardware features while preserving blocked programming productivity, with kernels deployed in large-scale training and inference.
-
Sim-FA: A GPGPU Simulator Framework for Fine-Grained FlashAttention Pipeline Analysis
Sim-FA is a new simulator that instruments FlashAttention-3 for cycle-accurate GPGPU analysis, achieving 5.7% average error on H800 while explaining inaccuracies in existing DRAM traffic models.
-
Microbenchmark-Driven Analytical Performance Modeling Across Modern GPU Architectures
Microbenchmark-driven analytical models for B200 and MI300A achieve 1.31% and 0.09% MAE on validation kernels, far outperforming roofline baselines exceeding 95% error.