Flexible Performant GEMM Kernels on GPUs

09/25/2020
by   Thomas Faingnaert, et al.
0

General Matrix Multiplication or GEMM kernels take centre place in high performance computing and machine learning. Recent NVIDIA GPUs include GEMM accelerators, such as NVIDIA's Tensor Cores. Their exploitation is hampered by the two-language problem: it requires either low-level programming which implies low programmer productivity or using libraries that only offer a limited set of components. Because rephrasing algorithms in terms of established components often introduces overhead, the libraries' lack of flexibility limits the freedom to explore new algorithms. Researchers using GEMMs can hence not enjoy programming productivity, high performance, and research flexibility at once. In this paper we solve this problem. We present three sets of abstractions and interfaces to program GEMMs within the scientific Julia programming language. The interfaces and abstractions are co-designed for researchers' needs and Julia's features to achieve sufficient separation of concerns and flexibility to easily extend basic GEMMs in many different ways without paying a performance price. Comparing our GEMMs to state-of-the-art libraries cuBLAS and CUTLASS, we demonstrate that our performance is in the same ballpark of the libraries, and in some cases even exceeds it, without having to write a single line of code in CUDA C++ or assembly, and without facing flexibility limitations.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 18

08/23/2021

High Performance GPU Code Generation for Matrix-Matrix Multiplication using MLIR: Some Early Results

This report presents some early results on code generation targeting ten...
03/13/2020

Fireiron: A Scheduling Language for High-Performance Linear Algebra on GPUs

Achieving high-performance GPU kernels requires optimizing algorithm imp...
06/22/2020

Automatic Kernel Generation for Volta Tensor Cores

A commonly occurring computation idiom in neural networks is to perform ...
10/22/2018

High-level Cryptographic Abstractions

The interfaces exposed by commonly used cryptographic libraries are clum...
03/15/2020

Towards automated kernel selection in machine learning systems: A SYCL case study

Automated tuning of compute kernels is a popular area of research, mainl...
10/04/2020

High level programming abstractions for leveraging hierarchical memories with micro-core architectures

Micro-core architectures combine many low memory, low power computing co...
06/30/2020

Extending the OpenCHK Model with Advanced Checkpoint Features

One of the major challenges in using extreme scale systems efficiently i...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.