Loop Quasi-Invariant Chunk Motion by peeling with statement composition

Several techniques for analysis and transformations are used in compilers. Among them, the peeling of loops for hoisting quasi-invariants can be used to optimize generated code, or simply ease developers' lives. In this paper, we introduce a new concept of dependency analysis borrowed from the field of Implicit Computational Complexity (ICC), allowing to work with composed statements called Chunks to detect more quasi-invariants. Based on an optimization idea given on a WHILE language, we provide a transformation method - reusing ICC concepts and techniques - to compilers. This new analysis computes an invariance degree for each statement or chunks of statements by building a new kind of dependency graph, finds the maximum or worst dependency graph for loops, and recognizes if an entire block is Quasi-Invariant or not. This block could be an inner loop, and in that case the computational complexity of the overall program can be decreased. We already implemented a proof of concept on a toy C parser 1 analysing and transforming the AST representation. In this paper, we introduce the theory around this concept and present a prototype analysis pass implemented on LLVM. In a very near future, we will implement the corresponding transformation and provide benchmarks comparisons.Comment: In Proceedings DICE-FOPARA 2017, arXiv:1704.0516

Un framework pour l'exécution efficace d'applications sur GPU et CPU+GPU

Technological limitations faced by the semi-conductor manufacturers in the early 2000's restricted the increase in performance of the sequential computation units. Nowadays, the trend is to increase the number of processor cores per socket and to progressively use the GPU cards for highly parallel computations. Complexity of the recent architectures makes it difficult to statically predict the performance of a program. We describe a reliable and accurate parallel loop nests execution time prediction method on GPUs based on three stages: static code generation, offline profiling, and online prediction. In addition, we present two techniques to fully exploit the computing resources at disposal on a system. The first technique consists in jointly using CPU and GPU for executing a code. In order to achieve higher performance, it is mandatory to consider load balance, in particular by predicting execution time. The runtime uses the profiling results and the scheduler computes the execution times and adjusts the load distributed to the processors. The second technique, puts CPU and GPU in a competition: instances of the considered code are simultaneously executed on CPU and GPU. The winner of the competition notifies its completion to the other instance, implying the termination of the latter.Les verrous technologiques rencontrés par les fabricants de semi-conducteurs au début des années deux-mille ont abrogé la flambée des performances des unités de calculs séquentielles. La tendance actuelle est à la multiplication du nombre de cœurs de processeur par socket et à l'utilisation progressive des cartes GPU pour des calculs hautement parallèles. La complexité des architectures récentes rend difficile l'estimation statique des performances d'un programme. Nous décrivons une méthode fiable et précise de prédiction du temps d'exécution de nids de boucles parallèles sur GPU basée sur trois étapes : la génération de code, le profilage offline et la prédiction online. En outre, nous présentons deux techniques pour exploiter l'ensemble des ressources disponibles d'un système pour la performance. La première consiste en l'utilisation conjointe des CPUs et GPUs pour l'exécution d'un code. Afin de préserver les performances il est nécessaire de considérer la répartition de charge, notamment en prédisant les temps d'exécution. Le runtime utilise les résultats du profilage et un ordonnanceur calcule des temps d'exécution et ajuste la charge distribuée aux processeurs. La seconde technique présentée met le CPU et le GPU en compétition : des instances du code cible sont exécutées simultanément sur CPU et GPU. Le vainqueur de la compétition notifie sa complétion à l'autre instance, impliquant son arrêt

LIPIcs, Volume 258, SoCG 2023, Complete Volume

LIPIcs, Volume 258, SoCG 2023, Complete Volum

The 2004 NASA Faculty Fellowship Program Research Reports

This is the administrative report for the 2004 NASA Faculty Fellowship Program (NFFP) held at the George C. Marshall Space Flight Center (MSFC) for the 40th consecutive year. The NFFP offers science and engineering faculty at U.S. colleges and universities hands-on exposure to NASA s research challenges through summer research residencies and extended research opportunities at participating NASA research Centers. During this program, fellows work closely with NASA colleagues on research challenges important to NASA's strategic enterprises that are of mutual interest to the fellow and the Center. The nominal starting and .nishing dates for the 10-week program were June 1 through August 6, 2004. The program was sponsored by NASA Headquarters, Washington, DC, and operated under contract by The University of Alabama, The University of Alabama in Huntsville, and Alabama A&M University. In addition, promotion and applications are managed by the American Society for Engineering Education (ASEE) and assessment is completed by Universities Space Research Association (USRA). The primary objectives of the NFFP are to: Increase the quality and quantity of research collaborations between NASA and the academic community that contribute to the Agency s space aeronautics and space science mission. Engage faculty from colleges, universities, and community colleges in current NASA research and development. Foster a greater public awareness of NASA science and technology, and therefore facilitate academic and workforce literacy in these areas. Strengthen faculty capabilities to enhance the STEM workforce, advance competition, and infuse mission-related research and technology content into classroom teaching. Increase participation of underrepresented and underserved faculty and institutions in NASA science and technology

PSA 2016

These preprints were automatically compiled into a PDF from the collection of papers deposited in PhilSci-Archive in conjunction with the PSA 2016

Efficient Domain Partitioning for Stencil-based Parallel Operators

Partial Differential Equations (PDEs) are used ubiquitously in modelling natural phenomena. It is generally not possible to obtain an analytical solution and hence they are commonly discretized using schemes such as the Finite Difference Method (FDM) and the Finite Element Method (FEM), converting the continuous PDE to a discrete system of sparse algebraic equations. The solution of this system can be approximated using iterative methods, which are better suited to many sparse systems than direct methods. In this thesis we use the FDM to discretize linear, second order, Elliptic PDEs and consider parallel implementations of standard iterative solvers. The dominant paradigm in this field is distributed memory parallelism which requires the FDM grid to be partitioned across the available computational cores. The orthodox approach to domain partitioning aims to minimize only the communication volume and achieve perfect load-balance on each core. In this work, we re-examine and challenge this traditional method of domain partitioning and show that for well load-balanced problems, minimizing only the communication volume is insufficient for obtaining optimal domain partitions. To this effect we create a high-level, quasi-cache-aware mathematical model that quantifies cache-misses at the sub-domain level and minimizes them to obtain families of high performing domain decompositions. To our knowledge this is the first work that optimizes domain partitioning by analyzing cache misses, establishing a relationship between cache-misses and domain partitioning. To place our model in its true context, we identify and qualitatively examine multiple other factors such as the Least Recently Used policy, Cache Line Utilization and Vectorization, that influence the choice of optimal sub-domain dimensions. Since the convergence rate of point iterative methods, such as Jacobi, for uniform meshes is not acceptable at a high mesh resolution, we extend the model to Parallel Geometric Multigrid (GMG). GMG is a multilevel, iterative, optimal algorithm for numerically solving Elliptic PDEs. Adaptive Mesh Refinement (AMR) is another multilevel technique that allows local refinement of a global mesh based on parameters such as error estimates or geometric importance. We study a massively parallel, multiphysics, multi-resolution AMR framework called BoxLib, and implement and discuss our model on single level and adaptively refined meshes, respectively. We conclude that “close to 2-D” partitions are optimal for stencil-based codes on structured 3-D domains and that it is necessary to optimize for both minimizing cache-misses and communication. We advise that in light of the evolving hardware-software ecosystem, there is an imperative need to re-examine conventional domain partitioning strategies

LIPIcs, Volume 244, ESA 2022, Complete Volume

LIPIcs, Volume 244, ESA 2022, Complete Volum

Haptics: Science, Technology, Applications

This open access book constitutes the proceedings of the 13th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2022, held in Hamburg, Germany, in May 2022. The 36 regular papers included in this book were carefully reviewed and selected from 129 submissions. They were organized in topical sections as follows: haptic science; haptic technology; and haptic applications