Transformations Déclaratives dans le Modèle Polyédrique

Despite the availability of sophisticated automatic optimizers, performance-critical code sections are in practice still tuned by human experts. Pragma-based languages such as OpenMP or OpenACC are the standard interface to apply such transformations to large code bases and loop transformation pragmas would be a straightforward extension to provide fine-grained control over a compilers loop optimizer. However, the manual optimization of programs via explicit sequences of directives is unlikely to fully solve this problem as expressing complex optimization sequences explicitly results in difficult to read and non-performance-portable code. We address this problem by presenting a novel framework of composable program transformations based on the internal tree-like program representation of a polyhedral compiler. Based on a set of tree matchers and transformers, we describe an embedded transformation language which provides the foundation for the development of program optimization tactics. Using this language, we express core building blocks such as loop tiling, fusion, or data-layout-transformations, and compose them to higher-level transformations expressing algorithm-specific optimization strategies for stencils, dense linear-algebra, etc. We expect our approach to simplify the development of polyhedral optimizers and integration of polyhedral and syntactic approaches.Malgré l’existence d’outils sophistiqués d’optimisation automatique, les parties des programmes dont la performance est cruciale sont toujoursoptimisées manuellement par des humains experts. Les langages basés sur des directives “pragma”, tels que OpenMP ou OpenACC, sont une interface typique pour exprimer les transformations sur des grandes bases de code source. Telles directives pour transformer des nids de boucles seraient une extension naturelle permettant de contrôler l’optimiseur de boucles d’une manière précise. Pourtant l’optimisation manuelle des programmes à travers les séquences des directives de transformation n’est pas toujours souhaitable car ces séquences longues et complexes produisent des programmes peu lisibles et ne bénéficiant pas de la portabilité de performance entre les différentes architectures matérielles. Nous proposons une nouvelle approche pour définir les transformations composables des programmes basée sur la représentation interne d’un compilateur polyédrique sous forme de l’arbre. Grâce à un ensemble des “motifs” et “transformateurs” des arbres, nous décrivons un langage de transformation sur lequel nous basons le développement des tactiques d’optimisation. Avec ce langage, il est possible d’exprimer les transformations basiques, telles que le tuilage, la fusion ou la transposition de données, ainsi que la composition de ces transformations afin de définir une stratégie d’optimisation pour les grandes classes des programmes, telles que les pochoirs, les contractions de tenseurs, etc. Notre approche pourrait simplifier le développement des optimiseurs polyédriques et l’intégration des transformations polyédriques et syntaxique

Leaping on urban islands: further summer and winter range expansion of European bat species

Cities or urban areas are the new types of landscapes that have rapidly developed in the Anthropocene and generally mimic mountains and rock habitats. Such areas attract different vertebrate species that naturally prefer rocky habitats, for example, bats, which are common animal inhabitants of the cities in the Northern Hemisphere. Here we review records of four bat species (Hypsugo savii, Plecotus austriacus, Pipistrellus nathusii and P. pygmaeus) inhabiting human settlements in Ukraine, encompassing the period from 2011 to 2022. Over the last 20 to 30 years, the winter range of P. nathusii has shifted 200-300 km north, and now covers all Black Sea coast steppe regions of continental Ukraine. The Pl. austiacus range most likely covers the whole territory of Ukraine. We documented the first factual records of H. savii in continental Ukraine and the first winter records of P. pygmaeus for the country. Our observations clearly demonstrate colonization of newly formed urban landscapes by bats species from different ecological groups. Therefore, bats, same as some other mammalian species, can be considered beneficiaries of urbanization and urban heat islands.&nbsp

Progressive Raising in Multi-level IR

International audienceMulti-level intermediate representations (IR) show great promise for lowering the design costs for domain-specific compilers by providing a reusable, extensible, and non-opinionated framework for expressing domain-specific and high-level abstractions directly in the IR. But, while such frameworks support the progressive lowering of high-level representations to low-level IR, they do not raise in the opposite direction. Thus, the entry point into the compilation pipeline defines the highest level of abstraction for all subsequent transformations, limiting the set of applicable optimizations, in particular for general-purpose languages that are not semantically rich enough to model the required abstractions. We propose Progressive Raising, a complementary approach to the progressive lowering in multi-level IRs that raises from lower to higher-level abstractions to leverage domain-specific transformations for low-level representations. We further introduce Multi-Level Tactics, our declarative approach for progressive raising, implemented on top of the MLIR framework, and demonstrate the progressive raising from affine loop nests specified in a general-purpose language to high-level linear algebra operations. Our raising paths leverage subsequent high-level domain-specific transformations with significant performance improvements

Unified Polyhedral Modeling of Temporal and Spatial Locality

Despite decades of work in this area, the construction of effective loop nest optimizers and parallelizers continues to be challenging due to the increasing diversity of both loop-intensive application workloads and complex memory/computation hierarchies in modern processors. The lack of a systematic approach to optimizing locality and parallelism, with a well-founded data locality model, is a major obstacle to the design of optimizing compilers coping with the variety of software and hardware. Acknowledging the conflicting demands on loop nest optimization, we propose a new unified algorithm for optimizing parallelism and locality in loop nests, that is capable of modeling temporal and spatial effects of multiprocessors and accelerators with deep memory hierarchies and multiple levels of parallelism. It orchestrates a collection of parameterizable optimization problems for locality and parallelism objectives over a polyhedral space of semantics-preserving transformations. The overall problem is not convex and is only constrained by semantics preservation. We discuss the rationale for this unified algorithm, and validate it on a collection of representative computational kernels/benchmarks.Malgré les décennies de travail dans ce domaine, la construction de compilateurs capables de paraléliser et optimiser les nids de boucle reste un problème difficile, dans le contexte d’une augmentation de la diversité des applications calculatoires et de la complexité de la hiérarchie de calcul et de stockage des processeurs modernes. L’absence d’une méthode systématique pour optimiser la localité et le parallélisme, fondée sur un modèle de localité des données pertinent, constitue un obstacle majeur pour prendre en charge la variété des besoins en optimisation de boucles issus du logiciel et du matériel. Dans ce contexte, nous proposons un nouvel algorithme unifié pour l’optimisation du parallélisme et de la localité dans les nids de boucles, capable de modéliser les effets temporels et spatiaux des multiprocesseurs et accélérateurs comportant des hiérarchies profondes de parallélisme et de mémoire. Cet algorithme coordonne la résolution d’une collection de problèmes d’optimisation paramètrés, portant sur des objectifs de localité ou et de parallélisme, dans un espace polyédrique de transformations préservant la sémantique du programme. La conception de cet algorithme fait l’objet d’une discussion systématique, ainsi que d’une validation expérimentale sur des noyaux calculatoires et benchmarks représentatifs

TC-CIM: Empowering Tensor Comprehensions for Computing-In-Memory

International audienceMemristor-based, non-von-Neumann architectures performing tensor operations directly in memory are a promising approach to address the ever-increasing demand for energy-efficient, high-throughput hardware accelerators for Machine Learning (ML) inference. A major challenge for the programmability and exploitation of such Computing-In-Memory (CIM) architectures consists in the efficient mapping of tensor operations from high-level ML frameworks to fixed-function hardware blocks implementing in-memory computations. We demonstrate the programmability of memristor-based accelerators with TC-CIM, a fully-automatic, end-to-end compilation flow from Tensor Comprehensions, a mathematical notation for tensor operations, to fixed-function memristor-based hardware blocks. Operations suitable for acceleration are identified using Loop Tactics, a declarative framework to describe computational patterns in a poly-hedral representation. We evaluate our compilation flow on a system-level simulator based on Gem5, incorporating crossbar arrays of memristive devices. Our results show that TC-CIM reliably recognizes tensor operations commonly used in ML workloads across multiple benchmarks in order to offload these operations to the accelerator