Hugo TABOADA
Hugo Taboada est ingénieur-chercheur au CEA. Il reçoit sont doctorat en informatique de l’université de Bordeaux en 2018 et rejoint le CEA la même année. Son travail consiste à aider les applications à bénéficier des spécificités des architectures et des supports exécutifs. Ses thématiques de recherche sont l’ordonnancement, le placement de threads, le recouvrement des communications, la projection de performance et l’interaction des language spécifique avec les runtimes. Il participe au Technical Manager Board du projet européen RED-SEA. Il participe aussi au MPI Forum, aidant à l’élaboration du prochain standard.
Hugo Taboada encadre actuellement 2 thèses, un ingénieur en CDD dans le cadre du projet européen RED-SEA ainsi que plusieurs stagiaires. Il est co-auteur de plusieurs publications dans des conférences internationales.
Abstract
High-Performance Computing (HPC) is currently facing significant challenges. The hardware pressure has become increasingly difficult to manage due to the lack of parallel abstractions in applications. As a result, parallel programs must undergo drastic evolution to effectively exploit underlying hardware parallelism. Failure to do so results in inefficient code. In this pressing environment, parallel runtimes play a critical role, and their esting becomes crucial. This paper focuses on the MPI interface and leverages the MPI binding tools to develop a multi-language test-suite for MPI. By doing so and building on previous work from the Forum’s document editors, we implement a systematic testing of MPI symbols in the context of the Parallel Computing Validation System (PCVS), which is an HPC validation platform dedicated to running and managing test-suites at scale. We first describe PCVS, then outline the process of generating the MPI API test suite, and finally, run these tests at scale. All data sets, code generators, and implementations are made available in open-source to the community. We also set up a dedicated website showcasing the results, which self-updates thanks to the Spack package manager.
ISC High Performance 2023: High Performance Computing pp 28–41, 2023
abstract
Abstract
The field of High-Performance Computing is rapidly evolving, driven by the race for computing power and the emergence of new architectures. Despite these changes, the process of launching programs has remained largely unchanged, even with the rise of hybridization and accelerators. However, there is a need to express more complex deployments for parallel applications to enable more efficient use of these machines. In this paper, we propose a transparent way to express malleability within MPI applications. This process relies on MPI process virtualization, facilitated by a dedicated privatizing compiler and a user-level scheduler. With this framework, using the MPC thread-based MPI context, we demonstrate how code can mold its resources without any software changes, opening the door to transparent MPI malleability. After detailing the implementation and associated interface, we present performance results on representative applications.
Euro-Par 2022: Parallel Processing - 28th International Conference on Parallel and Distributed Computing, Glasgow, UK, August 22-26, 2022, Proceedings, Springer, p. 85-99, 2022
Proceedings of SBAC-PAD 2022, IEEE, 2022
abstract
Abstract
HPC systems have experienced significant growth over the past years, with modern machines having hundreds of thousands of nodes. Message Passing Interface (MPI) is the de facto standard for distributed computing on these architectures. On the MPI critical path, the message-matching process is one of the most time-consuming operations. In this process, searching for a specific request in a message queue represents a significant part of the communication latency. So far, no miracle algorithm performs well in all cases. This paper explores potential matching specializations thanks to hints introduced in the latest MPI 4.0 standard. We propose a hash-table-based algorithm that performs constant time message-matching for no wildcard requests. This approach is suitable for intensive point-to-point communication phases in many applications (more than 50% of CORAL benchmarks). We demonstrate that our approach can improve the overall execution time of real HPC applications by up to 25%. Also, we analyze the limitations of our method and propose a strategy for identifying the most suitable algorithm for a given application. Indeed, we apply machine learning techniques for classifying applications depending on their message pattern characteristics.
Abstract
Heterogeneous supercomputers are now considered the most valuable solution to reach the Exascale. Nowadays, we can frequently observe that compute nodes are composed of more than one GPU accelerator. Programming such architectures efficiently is challenging. MPI is the defacto standard for distributed computing. CUDAaware libraries were introduced to ease GPU inter-nodes communications. However, they induce some overhead that can degrade overall performances. MPI 4.0 Specification draft introduces the MPI Sessions model which offers the ability to initialize specific resources for a specific component of the application. In this paper, we present a way to reduce the overhead induced by CUDA-aware libraries with a solution inspired by MPI Sessions. In this way, we minimize the overhead induced by GPUs in an MPI context and allow to improve CPU + GPU programs efficiency. We evaluate our approach on various micro-benchmarks and some proxy applications like Lulesh, MiniFE, Quicksilver, and Cloverleaf. We demonstrate how this approach can provide up to a 7x speedup compared to the standard MPI model.
Int. J. High Perform. Comput. Appl., 2019
Proceedings of the 26th European MPI Users’ Group Meeting, EuroMPI 2019, Zürich, Switzerland, September 11-13, 2019, ACM, p. 10:1-10:10, 2019
Euro-Par 2018: Parallel Processing - 24th International Conference on Parallel and Distributed Computing, Turin, Italy, August 27-31, 2018, Proceedings, Springer, p. 616-627, 2018
Euro-Par 2018: Parallel Processing Workshops - Euro-Par 2018 International Workshops, Turin, Italy, August 27-28, 2018, Revised Selected Papers, Springer, p. 123-133, 2018