Sparse Matrix-Vector Multiplication (SpMV) is a critical computational kernel in scientific computing, graph processing, and machine learning. Despite its importance, SpMV achieves only a small fraction of peak performance, varying substantially across matrices and architectures. This paper comprehensively analyzes SpM...
Panagiotis Mpakos, Dimitrios Galanopoulos, Petros Anastasiadis et al.· ACM Transactions on Architec...· 0 citations
An automated framework is introduced that jointly explores compiler flags and runtime parameters using both Bayesian optimization and a tabu-search–based strategy to investigate the transferability of compile-time and runtime autotuning decisions across CPUs and GPUs, focusing on AdaptiveCpp, a SYCL implementation buil...
Hari Abram, Nikela Papadopoulou, Jens Domke et al.· International Conference on...· 0 citations
This paper presents FlashAttention-V, a blocked FlashAttention for scalable vector architectures that adapts efficiently from short to very long vectors by exploiting parallelism across attention heads, inter-head packing to enable efficient utilization of vector lengths beyond the head dimension, and improving vector...
