Automatic parallelization of structured if statements without if conversion

Projet CHLOERésumé disponible dans le fichier PD

Data Dependence Analysis of Assembly Code

Determination of data dependences is a task typically performed with high-leve- l language source code in today's optimizing and parallelizing compilers. Very little work has been done in the field of data dependence analysis on assembly language code, but this area will be of growing importance, e.g. for increasing ILP. A central element of a data dependence analysis in this case is a method for memory reference disambiguation which decides whether two memory operations may/must access the same memory location. In this paper we describe a new approach for determination of data dependences in assembly code. Our method is based on a sophisticated algorithm for symbolic value propagation, and it can derive value-based dependences between memory operations instead of just address-based dependences. We have integrated our method into the SALTO system for assembly language optimization. Experimental results show that our approach greatly improves the accuracy of the dependence analysis in many cases

The SANDRA project: cooperative architecture/compiler technology for embedded real-time streaming applications

The convergence of digital television, Internet access, gaming, and digital media capture and playback stresses the importance of high-quality and high-performance video and graphics processing. The SANDRA project, a collaboration between Philips Research and INRIA, develops a consistent and efficient system design approach for regular, real-time constrained stream processing. The project aims at providing a system template with its associated compiler chain and application development framework, enabling an early validation of both the functional and the non-functional requirements of the application at every system design stage

Extended Performance Analysis

Résumé disponible dans le fichier attach

Register Saturation in Data Dependence Graphs

Register constraints in ILP scheduling can be taken into account during the scheduling phase of a code. The complexity of this problem is very high. In this work, we present a new approach consisting in manipulating data dependence graphs to reduce the number of "potential" values simultaneously alive without assuming any schedule. We study theoretically the exact upper-bound of the register need for all valid schedules of a code : we call this limit the register saturation. It is used to build a modified data dependence graph such that any schedule of this graph will verify the register constraints and avoid introducing spill code. We study the case of Direct Acyclic Graphs and then we extend it to loops intended to software pipelining schedule. Experimental study shows that many DAGs and loops do not need register constraints during scheduling