Philippe Clauss

Profiling Data-Dependence to Assist Parallelization: Framework, Scope, and Optimization

This paper describes a tool using one or more executions of a sequential program to detect parallel portions of the program. The tool, called Par wiz, uses dynamic binary instrumentation, targets various forms of parallelism, and suggests distinct parallelization actions, ranging from simple directive tagging to elaborate loop transformations. The first part of the paper details the link between the programs static structures (like routines and loops), the memory accesses performed by the program, and the dependencies that are used to highlight potential parallelism. This part also describes the instrumentation involved, and the general architecture of the system. The second part of the paper puts the framework into action. The first study focuses on loop parallelism, targeting OpenMP parallel-for directives, including privatization when necessary. The second study is an adaptation of a well-known vectorization technique based on a slightly richer dependence description, where the tool suggests an elaborate loop transformation. The third study views loops as a graph of (hopefully lightly) dependent iterations. The third part of the paper explains how the overall cost of data-dependence profiling can be reduced. This cost has two major causes: first, instrumenting memory accesses slows down the program, and second, turning memory accesses into dependence graphs consumes processing time. Par wiz uses static analysis of the original (binary) program to provide data at a coarser level, moving from individual accesses to complete loops whenever possible, thereby reducing the impact of both sources of inefficiency.

A symbolic approach to Bernstein expansion for program analysis and optimization

Several mathematical frameworks for static analysis of programs have been developed in the past decades. Although these tools are quite useful, they have still many limitations. In particular, integer multi-variate polynomials arise in many situations while analyzing programs, and analysis systems are unable to handle such expressions. Although some dedicated methods have already been proposed, they only handle some subsets of such expressions. This paper presents an original and general approach to Bernstein expansion which is symbolic. Bernstein expansion allows bounding the range of a multivariate polynomial over a box and is generally more accurate than classic interval methods.

Recovering the Memory Behavior of Executable Programs

This paper deals with the binary analysis of executable programs, with the goal of understanding how they access memory. It explains how to statically build a formal model of all memory accesses. Starting with a control-flow graph of each procedure, well-known techniques are used to structure this graph into a hierarchy of loops in all cases. The paper shows that much more information can be extracted by performing a complete data-flow analysis over machine registers after the program has been put in static single assignment (SSA) form. By using the SSA form, registers used in addressing memory can be symbolically expressed in terms of other, previously set registers. By including the loop structures in the analysis, loop indices and trip counts can also often be expressed symbolically. The whole process produces a formal model made of loops where memory accesses are linear expressions of loop counters and registers. The paper provides a quantitative evaluation of the results when applied to several dozens of SPEC benchmark programs. Because static analysis is often incomplete, the paper ends by describing a lightweight instrumentation strategy that collects at run time enough information to complete the programs symbolic description.