GPU kernels have come to the forefront of comput- ing due to their utility in
varied fields, from high-performance computing to machine learning. A typical
GPU compute kernel is invoked millions, if not billions of times in a typical
application, which makes their performance highly critical. Due to the unknown
nature of the optimization surface, an exhaustive search is required to
discover the global optimum, which is infeasible due to the possible
exponential number of parameter combinations. In this work, we propose a
methodology that uses deep sequence- to-sequence models to predict the optimal
tuning parameters governing compute kernels. This work considers the prediction
of kernel parameters as a sequence to the sequence translation problem,
borrowing models from the Natural Language Process- ing (NLP) domain.
Parameters describing the input, output and weight tensors are considered as
the input language to the model that emits the corresponding kernel parameters.
In essence, the model translates the problem parameter language to kernel
parameter language. The core contributions of this work are: a) Proposing that
a sequence to sequence model can accurately learn the performance dynamics of a
GPU compute kernel b) A novel network architecture which predicts the kernel
tuning parameters for GPU kernels, c) A constrained beam search which
incorporates the physical limits of the GPU hardware as well as other expert
knowledge reducing the search space. The proposed algorithm can achieve more
than 90% accuracy on various convolutional kernels in MIOpen, the AMD machine
learning primitives library. As a result, the proposed technique can reduce the
development time and compute resources required to tune unseen input
configurations, resulting in shorter development cycles, reduced development
costs, and better user experience.

使用深度序列到序列模型预测 GPU 计算内核的最佳调优参数，能够准确学习 GPU 计算内核的性能动态，减少开发时间和计算资源。

使用深度序列模型预测最佳核调参

Optimal Kernel Tuning Parameter Prediction using Deep Sequence Models

We describe MGARD, a software providing MultiGrid Adaptive Reduction for
floating-point scientific data on structured and unstructured grids. With
exceptional data compression capability and precise error control, MGARD
addresses a wide range of requirements, including storage reduction,
high-performance I/O, and in-situ data analysis. It features a unified
application programming interface (API) that seamlessly operates across diverse
computing architectures. MGARD has been optimized with highly-tuned GPU kernels
and efficient memory and device management mechanisms, ensuring scalable and
rapid operations.

MGARD 是一种提供了针对结构化和非结构化网格上的浮点科学数据的多网格自适应降维的软件，具有出色的数据压缩能力和精确的误差控制，可用于存储减少、高性能输入 / 输出和原位数据分析。它采用统一的应用程序编程接口 (API)，可以在不同的计算架构上无缝操作。MGARD 经过了高度优化的 GPU 内核和高效的内存和设备管理机制，保证了可伸缩和快速的操作。

MGARD：用于高性能、误差控制的数据压缩和重构的多格框架

MGARD: A multigrid framework for high-performance, error-controlled data  compression and refactoring

Scientific workloads have traditionally exploited high levels of sparsity to
accelerate computation and reduce memory requirements. While deep neural
networks can be made sparse, achieving practical speedups on GPUs is difficult
because these applications have relatively moderate levels of sparsity that are
not sufficient for existing sparse kernels to outperform their dense
counterparts. In this work, we study sparse matrices from deep learning
applications and identify favorable properties that can be exploited to
accelerate computation. Based on these insights, we develop high-performance
GPU kernels for two sparse matrix operations widely applicable in neural
networks: sparse matrix-dense matrix multiplication and sampled dense-dense
matrix multiplication. Our kernels reach 27% of single-precision peak on Nvidia
V100 GPUs. Using our kernels, we demonstrate sparse Transformer and MobileNet
models that achieve 1.2-2.1x speedups and up to 12.8x memory savings without
sacrificing accuracy.

本文提出一种基于稀疏矩阵的计算优化方法，通过对深度学习应用中的稀疏矩阵进行深入研究，开发出可用于稀疏矩阵与密集矩阵乘法和采样密集 - 密集矩阵乘法的高性能 GPU 核，实现了神经网络模型的加速和内存节省。