20 results for “fast matrix multiplication”
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The paper proposes a scale-invariant scaling formula for Ozaki scheme II to emulate high-precision matrix multiplication using low-precision integer matrix operations, ensuring CRT uniqueness conditio…
The paper introduces $g$MAGNUS, a new algorithm for sparse matrix-matrix multiplication on GPUs that addresses heavy rows by reordering intermediate products and achieves significant speedups.
This paper proposes methods to optimally permute the rows and columns of a sparse matrix to minimize the number of cyclic diagonals required for homomorphic sparse-matrix vector multiplication, signif…
Hawkeye is a system that allows perfect, precision-preserving reproduction of GPU-level matrix multiplication operations on a CPU, enabling efficient and trustworthy third-party auditing of machine le…
This paper presents a local computation algorithm to approximate the top eigenvector of a symmetric matrix with entries between -1 and 1, building on Swartworth and Woodruff's work.
This paper presents a method for compressing matrices using a RePair straight-line program (SLP), allowing matrix-vector products with time and space proportional to the compressed size, and demonstra…
The paper introduces Fast-TurboQuant, a multiplier-free projection architecture for large language models that uses a structured fast Johnson-Lindenstrauss transform instead of dense matrices, resulti…
The paper proposes a provably secure, single-round two-party computation protocol for approximate matrix multiplication using lattice-based cryptography, demonstrated for secure control law implementa…
The paper proposes a novel, optimized sparse matrix multiplication method for fully homomorphic encrypted deep neural networks, achieving up to a 3.0x speedup on AMD GPUs compared to CPU implementatio…
This paper reformulates cascaded second-order filtering as a block-tridiagonal linear system and develops parallel solution algorithms, achieving high performance on SIMD cores, multi-core CPUs, and G…
This paper introduces Stale Synchronous Parallel mode of execution for parallel sparse triangular linear system solve and presents a scheduler that achieves geometric-mean speed-ups of 7-30% over Grow…
The paper proposes a Secure Parallel Determinant Computation (SPDC) framework that enables efficient, privacy-preserving, and scalable matrix determinant calculation across multiple untrusted edge ser…
This paper introduces the Distributed Truncated Spectral Transform (DTST) for Fourier Neural Operators (FNOs), achieving significant speedups in distributed computing.
This paper connects a generalized notion of tensor rank with multiplicative complexity, enabling control of arithmetic complexity in any constant degree of multilinearity and applications to fine-grai…
This paper reports on SciGMark 1.5, a benchmark study of specialized and generic implementations in modern languages, examining the consequences of various generic-realization strategies and extending…
The paper describes how to compute singular value soft-thresholding using matrix polar decomposition for faster GPU processing.
O-POPE is a novel outer-product engine that accelerates floating-point GEMM by repurposing FPU pipeline registers as buffers, achieving high utilization and improved energy efficiency.