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Simple hardware-efficient long convolutions for sequence modeling
State space models (SSMs) have high performance on long sequence modeling but require
sophisticated initialization techniques and specialized implementations for high quality and …
sophisticated initialization techniques and specialized implementations for high quality and …
Flashfftconv: Efficient convolutions for long sequences with tensor cores
Acceleration of tensor-product operations with tensor cores
C Cui - ACM Transactions on Parallel Computing, 2024 - dl.acm.org
In this article, we explore the acceleration of tensor product operations in finite element
methods, leveraging the computational power of the NVIDIA A100 GPU Tensor Cores. We …
methods, leveraging the computational power of the NVIDIA A100 GPU Tensor Cores. We …
Bind the gap: Compiling real software to hardware FFT accelerators
Specialized hardware accelerators continue to be a source of performance improvement.
However, such specialization comes at a programming price. The fundamental issue is that …
However, such specialization comes at a programming price. The fundamental issue is that …
Accelerating range minimum queries with ray tracing cores
Over the past decade, GPU technology has undergone a notable transformation, evolving
from pure general-purpose computation to the integration of application-specific integrated …
from pure general-purpose computation to the integration of application-specific integrated …
Reducing shared memory footprint to leverage high throughput on Tensor Cores and its flexible API extension library
Matrix-matrix multiplication is used for various linear algebra algorithms such as matrix
decomposition and tensor contraction. NVIDIA Tensor Core is a mixed-precision matrix …
decomposition and tensor contraction. NVIDIA Tensor Core is a mixed-precision matrix …