A matrix math facility for Power ISA(TM) processors
Power ISA(TM) Version 3.1 has introduced a new family of matrix math instructions, collectively known as the Matrix-Multiply Assist (MMA) facility. The instructions in this facility implement numerical linear algebra operations on small matrices and are meant to accelerate computation-intensive kernels, such as matrix multiplication, convolution and discrete Fourier transform. These instructions have led to a power- and area-efficient implementation of a high throughput math engine in the future POWER10 processor. Performance per core is 4 times better, at constant frequency, than the previous generation POWER9 processor. We also advocate the use of compiler built-ins as the preferred way of leveraging these instructions, which we illustrate through case studies covering matrix multiplication and convolution.
Code (0)
등록된 구현이 없습니다.
Tasks
MathMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Strategic Facility Location with Clients that Minimize Total Waiting Time
We study a non-cooperative two-sided facility location game in which facilities and clients behave strategically. This is in contrast to many other facility location games in which clients simply visit their closest faci…
Improved Bounds for Online Facility Location with Predictions
We consider Online Facility Location in the framework of learning-augmented online algorithms. In Online Facility Location (OFL), demands arrive one-by-one in a metric space and must be (irrevocably) assigned to an open …
A Study of Clustering Techniques and Hierarchical Matrix Formats for Kernel Ridge Regression
We present memory-efficient and scalable algorithms for kernel methods used in machine learning. Using hierarchical matrix approximations for the kernel matrix the memory requirements, the number of floating point operat…
BIG-bench Machine LearningClusteringregressionBus Admittance Matrix Revisited: Is It Outdated on Modern Computers?
Bus admittance matrix is widely used in power engineering for modeling networks. Being highly sparse, it requires fewer CPU operations when used for calculations. Meanwhile, sparse matrix calculations involve numerous in…
CPUIn-Materia Speech Recognition
With the rise of decentralized computing, as in the Internet of Things, autonomous driving, and personalized healthcare, it is increasingly important to process time-dependent signals at the edge efficiently: right at th…
Autonomous Drivingspeech-recognitionSpeech Recognition