DeepAI AI Chat
Log In Sign Up

Comprehensive Optimization of Parametric Kernels for Graphics Processing Units

by   Xiaohui Chen, et al.

This work deals with the optimization of computer programs targeting Graphics Processing Units (GPUs). The goal is to lift, from programmers to optimizing compilers, the heavy burden of determining program details that are dependent on the hardware characteristics. The expected benefit is to improve robustness, portability and efficiency of the generated computer programs. We address these requirements by: (1) treating machine and program parameters as unknown symbols during code generation, and (2) generating optimized programs in the form of a case discussion, based on the possible values of the machine and program parameters. By taking advantage of recent advances in the area of computer algebra, preliminary experimentation yield promising results.


page 1

page 2

page 3

page 4


A Technique for Finding Optimal Program Launch Parameters Targeting Manycore Accelerators

In this paper, we present a new technique to dynamically determine the v...

KLARAPTOR: A Tool for Dynamically Finding Optimal Kernel Launch Parameters Targeting CUDA Programs

In this paper we present KLARAPTOR (Kernel LAunch parameters RAtional Pr...

Dual Reinforcement-Based Specification Generation for Image De-Rendering

Advances in deep learning have led to promising progress in inferring gr...

Generating Adversarial Computer Programs using Optimized Obfuscations

Machine learning (ML) models that learn and predict properties of comput...

Signatures of small-world and scale-free properties in large computer programs

A large computer program is typically divided into many hundreds or even...

3D Primitives Gpgpu Generation for Volume Visualization in 3D Graphics Systems

This article discusses the study of 3D graphic volume primitive computer...

A Learned Performance Model for Tensor Processing Units

Accurate hardware performance models are critical to efficient code gene...