APOLLO: Automatic speculative POLyhedral Loop Optimizer

International audienceA few weeks ago, we were glad to announce the first release of Apollo, the Automatic speculative POLyhedral Loop Opti-mizer. Apollo applies polyhedral optimizations on-the-fly to loop nests, whose control flow and memory access patterns cannot be determined at compile-time. In contrast to existing tools, Apollo can handle any kind of loop nest, whose memory accesses can be performed through pointers and in-directions. At runtime, Apollo builds a predictive polyhedral model, which is used for speculative optimization including parallelization. Being a dynamic system, Apollo can even apply the polyhedral model to nonlinear loops. This paper describes Apollo from the perspective of a user, as well as some of its main contributions and mechanisms, including the just-in-time polyhedral compilation, that significantly extends the scope of polyhedral techniques

Techniques de compilation rapides et flexibles pour une parallélisation polyédrique speculative efficace

In this thesis, we present our contributions to APOLLO: an automatic parallelization compiler that combines polyhedral optimization with Thread-Level-Speculation, to optimize dynamic codes on-the-fly. Thanks to an online profiling phase and a speculation model about the target's code behavior, Apollo is able to select an optimization and to generate code based on it. During optimized code execution, Apollo constantly verifies the validity of the speculation model. The main contribution of this thesis is a code generation mechanism that is able to instantiate any polyhedral transformation, at runtime, without incurring a major time-overhead. This mechanism is currently in use inside Apollo. We called it Code-Bones. It provides significant performance benefits when compared to other approaches.Dans cette thèse, nous présentons nos contributions à APOLLO: un compilateur de parallélisation automatique qui combine l'optimisation polyédrique et la parallélisation spéculative, afin d'optimiser des programmes dynamiques à la volée. Grâce à une phase de profilage en ligne et un modèle spéculatif du comportement mémoire du programme cible, Apollo est capable de sélectionner une optimisation et de générer le code résultant. Pendant l'exécution du programme optimisé, Apollo vérifie constamment la validité du modèle spéculatif. La contribution principale de cette thèse est un mécanisme de génération de code qui permet d'instancier toute transformation polyédrique, au cours de l'exécution du programme cible, sans engendrer de surcoût temporel majeur. Ce procédé est désormais utilisé dans Apollo. Nous l'appelons \textit{Code-Bones}. Il procure des gains de performance significatifs par comparaison aux autres approches

Code Bones: Fast and Flexible Code Generation for Dynamic and Speculative Polyhedral Optimization

International audienceIn this paper, we present a new runtime code generation technique for speculative loop optimization and parallelization, that allows to generate on-the-fly codes resulting from any polyhedral optimizing transformation of loop nests, such as tiling, skewing, fission, fusion or interchange, without introducing a penalizing time overhead. The proposed strategy is based on the generation of code bones at compile-time, which are parametrized code snippets either dedicated to speculation management or to computations of the original target program. These code bones are then instantiated and assembled at runtime to constitute the speculatively-optimized code, as soon as an optimizing polyhedral transformation has been determined. Their granularity threshold is sufficient to apply any polyhedral transformation, while still enabling fast runtime code generation. This strategy has been implemented in the speculative loop parallelizing framework Apollo

Online dynamic dependence analysis for speculative polyhedral parallelization

We present a dynamic dependence analyzer whose goal is to compute dependencies from instrumented execution samples of loop nests. The resulting information serves as a prediction of the execution behavior during the remaining iterations and can be used to select and apply a speculatively optimizing and parallelizing polyhedral transformation of the target sequential loop nest. Thus, a parallel lock-free version can be generated which should not induce any rollback if the prediction is correct. The dependence analyzer computes distance vectors and linear functions interpolating the memory addresses accessed by each memory instruction, and the values of some scalars. Phases showing a changing memory behavior are detected thanks to a dynamic adjustment of the instrumentation frequency. The dependence analyzer takes part of a whole framework dedicated to speculative parallelization of loop nests which has been implemented with extensions of the LLVM compiler and an x86-64 runtime system.UPMAR

APOLLO: Automatic speculative POLyhedral Loop Optimizer

International audienceA few weeks ago, we were glad to announce the first release of Apollo, the Automatic speculative POLyhedral Loop Opti-mizer. Apollo applies polyhedral optimizations on-the-fly to loop nests, whose control flow and memory access patterns cannot be determined at compile-time. In contrast to existing tools, Apollo can handle any kind of loop nest, whose memory accesses can be performed through pointers and in-directions. At runtime, Apollo builds a predictive polyhedral model, which is used for speculative optimization including parallelization. Being a dynamic system, Apollo can even apply the polyhedral model to nonlinear loops. This paper describes Apollo from the perspective of a user, as well as some of its main contributions and mechanisms, including the just-in-time polyhedral compilation, that significantly extends the scope of polyhedral techniques

APOLLO: Automatic speculative POLyhedral Loop Optimizer

International audienceA few weeks ago, we were glad to announce the first release of Apollo, the Automatic speculative POLyhedral Loop Opti-mizer. Apollo applies polyhedral optimizations on-the-fly to loop nests, whose control flow and memory access patterns cannot be determined at compile-time. In contrast to existing tools, Apollo can handle any kind of loop nest, whose memory accesses can be performed through pointers and in-directions. At runtime, Apollo builds a predictive polyhedral model, which is used for speculative optimization including parallelization. Being a dynamic system, Apollo can even apply the polyhedral model to nonlinear loops. This paper describes Apollo from the perspective of a user, as well as some of its main contributions and mechanisms, including the just-in-time polyhedral compilation, that significantly extends the scope of polyhedral techniques

Speculative program parallelization with scalable and decentralized runtime verification

Thread Level Speculation (TLS) is a dynamic code parallelization technique proposed to keep the software in pace with the advances in hardware,in particular, to automatically parallelize programs to take advantage of the multi-core processors. Being speculative, frameworks of this type unavoidably rely on verification systems that are similar to software transactional memory, and that require voluminous inter-thread communications or centralized registering of the performed memory accesses. The high degree of communication is against the basic principles of high performance parallel computing, does not scale with an increasing number of processor cores, and yields weak performance. Moreover, TLS systems often apply one unique parallelization strategy consisting in slicing a loop into several parallel speculative threads. Such a strategy is also against the basic principles since loops in the original serial code are not necessarily parallel and also, it is well-known that the parallel schedule must promote data locality which is crucial in obtaining good performance. This situation appeals to scalable and decentralized verification systems and new strategies to dynamically generate efficient parallel code resulting from advanced optimizing parallelizing transformations. Such transformations require a more complex verification system that allows intra-thread iterations to be reordered. In this paper, we propose a verification system of this kind, based on a model built at runtime and predicting a linear memory behavior. This strategy is part of the Apollo speculative code parallelizer which is based on an adaptation for dynamic usage of the polyhedral model.UPMAR

Springback Estimation in the Hydroforming Process of UNS A92024-T3 Aluminum Alloy by FEM Simulations

The production of metal parts manufactured through the hydroforming process is strongly affected by the difficulty in predicting the elastic recovery (springback) of the material. In addition, the formation of wrinkles and crack growth should be avoided. Manual cold work is widely employed in industry to obtain the final shape of the manufactured parts. Therefore, an accurate springback estimation is of high interest to reduce the overall time of manufacturing and also to decrease the manual rectification stage. A working procedure based on finite element simulations (FEM) was developed to estimate the elastic recovery and predict the final morphology of UNS A92024-T3 aluminum alloy pieces after forming. Experimental results of real hydroformed parts were compared with the results obtained in simulations performed with PAM-STAMP software. The influence of different experimental parameters on the forming processes was also analyzed, such as the material properties, the rolling direction of sheet metal, or the hardening criteria employed to characterize the plastic region of the alloy. Results obtained in the present work show an excellent agreement between real and simulated tests, the maximum morphology deviations being less than the thickness of parts (2.5 mm). FEM simulations have become a suitable and mature tool that allows the prediction of the pieces springback, a precise material characterization being required to obtain reliable results

Dynamic and Speculative Polyhedral Parallelization of Loop Nests Using Binary Code Patterns

N.B. When citing this work, cite the original published paper. Permanent link to this version: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-206798 Available online at www.sciencedirect.co

Runtime Multi-versioning and Specialization inside a Memoized Speculative Loop Optimizer

International audienceIn this paper, we propose a runtime framework that implements code multi-versioning and specialization to optimize and parallelize loop kernels that are invoked many times with varying parameters. These parameters may influence the code structure, the touched memory locations, the work-load, and the runtime performance. They may also impact the validity of the parallelizing and optimizing polyhedral transformations that are applied on-the-fly. For a target loop kernel and its associated parameters, a different optimizing and parallelizing transformation is evaluated at each invocation, among a finite set of transformations (multi-versioning and specialization). The best performing transformed code version is stored and indexed using its associated parameters. When every optimizing transformation has been evaluated, the best performing code version regarding the current parameters, which has been stored, is relaunched at next invocations (memoization)