Characterization of vectorization strategies for recursive algorithms

Shruthi Balakrishna, Purdue University

Abstract

A successful architectural trend in parallelism is the emphasis on data parallelism with SIMD hardware. Since SIMD extensions on commodity processors tend to require relatively little extra hardware, executing a SIMD instruction is essentially free from a power perspective, making vector computation an attractive target for parallelism. SIMD instructions are designed to accelerate the performance of applications such as motion video, real-time physics and graphics. Such applications perform repetitive operations on large arrays of numbers. While the key idea is to parallelize significant portions of data that get operated by several sequential instructions into a single instruction, not every application can be parallelized automatically. Regular applications with dense matrices and arrays are easier to vectorize compared to irregular applications that involve pointer based data structures like trees and graphs. Programmers are burdened with the arduous task of manually tuning such applications for better performance. One such class of applications are recursive programs. While they are not traditional serial instruction sequences, they follow a serialized pattern in their control flow graph and exhibit dependencies. They can be visualized to be directed trees data structures. Vectorizing recursive applications with SIMD hardware cannot be achieved by using the existing intrinsic directly because of the nature of these algorithms. In this dissertation, we argue that, for an important subset of recursive programs which arise in many domains, there exists general techniques to efficiently vectorize the program to operate on SIMD architecture. Recursive algorithms are very popular in graph problems, tree traversal algorithms, gaming applications et al. While multi-core and GPU implementation of such algorithms have been explored, methods to execute them efficiently on vector units like SIMD and AVX have not been explored. We investigate techniques for work generation and efficient vectorization to enable vectorization in recursion. We further implement a generic tree model that allows us to guarantee lower bounds on its utilization efficiency.

Degree

M.S.E.C.E.

Advisors

Kulkarni, Purdue University.

Subject Area

Computer Engineering

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