Right off the bat, let’s give a shout out to the mathematician propeller-heads who create the transformations that make it possible to do all kinds of high performance computing to simulate, model, ...

Abstract: Efficiently synthesizing an entire application that consists of multiple algorithms for hardware implementation is a very difficult and unsolved problem. One of the main challenges is the ...

Abstract: Sparse general matrix multiplication (SpGEMM) is a fundamental building block for many real-world applications. Since SpGEMM is a well-known memory-bounded application with vast and ...

TPUs are Google’s specialized ASICs built exclusively for accelerating tensor-heavy matrix multiplication used in deep learning models. TPUs use vast parallelism and matrix multiply units (MXUs) to ...

This repository contains the CUDA kernels for general matrix-matrix multiplication (GEMM) and the corresponding performance analysis. The correctness of the CUDA kernels is guaranteed for any matrix ...

α-Calcium sulfate hemihydrate (α-CSH) is a widely used artificial bone graft material, but it suffers from rapid deterioration and limited osteoinductivity. This study aims to develop composite ...

Differential equations often give rise to rank-structured matrices characterized by low-rank off-diagonal blocks. These matrices can be conveniently represented in a hierarchical format, enabling ...

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now Matrix multiplications (MatMul) are the ...