arXiv subjects

José I. Aliaga

Publications and source records attributed to José I. Aliaga.

7 recordsLinked to original sources

arXiv2026

Towards the Democratization and Standardization of Dynamic Resources with MPI Spawning

This paper presents an efficient tool for managing dynamic resources in production high-performance computing (HPC) settings, focusing on flexibility, adaptability, and user-friendliness. We introduce a unified dynamic resource management application programming interface (API) that supports a wide range of HPC applications, allowing seamless integration without direct interaction with Dynamic Management of Resources (DMR). The DMR framework, evolved from the DMRlib structure, now supports various dynamic resource managers and includes the Proteo reconfiguration engine to enhance malleability strategies. This integration addresses previous limitations by allowing diverse reconfiguration methods without respawning all processes or lacking RMS support. The paper also showcases the solution's performance and coding productivity with the MPDATA (Multidimensional Positive Definite Advection Transport Algorithm) application. Key contributions include an enhanced modular DMR framework supporting different reconfiguration managers, upgraded DMRlib with the Proteo reconfiguration engine, offering extensive reconfiguration strategies, and a malleable version of the MPDATA solver.

cs.DC↗

arXiv2026

Parallel Spawning Strategies for Dynamic-Aware MPI Applications

Dynamic resource management is an increasingly important capability of High Performance Computing systems, as it enables jobs to adjust their resource allocation at runtime. This capability can reduce workload makespan, substantially decreasing job waiting times and optimizing resource allocation. In this context, malleability refers to the ability of applications to adapt to new resource allocations during execution. Although beneficial, malleability incurs significant reconfiguration costs, making the reduction of these costs an important research topic. Some existing solutions for MPI applications respawn the entire application, which is an expensive solution that avoids the reuse of original processes. Other MPI solutions reuse them, but fail to fully release unneeded processes when shrinking, since some ranks within the same communicator remain active across nodes, preventing the application from returning those nodes to the system. This work overcomes both limitations by proposing a novel parallel spawning strategy, in which all processes cooperate in the spawning. This allows expansions to reuse processes while also terminating unneeded ones. This strategy has been validated on two systems with either machines with equal or different numbers of cores. Experiments show that this strategy preserves competitive expansion times with at most a $1.13\times$ and $1.25\times$ overhead for equal and different number of cores per node, respectively. More importantly, it enables fast shrink operations that reduce their cost by at least $1387\times$ and $20\times$ in the same scenarios.

cs.DC↗

arXiv2026

Dynamic reconfiguration for malleable applications using RMA

This paper investigates the novel one-sided communication methods based on remote memory access (RMA) operations in MPI for dynamic resizing of malleable applications, enabling data redistribution with minimal impact on application execution. After their integration into the MaM library, these methods are compared with traditional collective-based approaches. In addition, the existing strategy Wait Drains is extended to support efficient background reconfiguration. Results show comparable performance, though high initialization costs currently limit their advantage.

cs.DC↗

arXiv2025

Resource Optimization with MPI Process Malleability for Dynamic Workloads in HPC Clusters

Dynamic resource management is essential for optimizing computational efficiency in modern high-performance computing (HPC) environments, particularly as systems scale. While research has demonstrated the benefits of malleability in resource management systems (RMS), the adoption of such techniques in production environments remains limited due to challenges in standardization, interoperability, and usability. Addressing these gaps, this paper extends our prior work on the Dynamic Management of Resources (DMR) framework, which provides a modular and user-friendly approach to dynamic resource allocation. Building upon the original DMRlib reconfiguration runtime, this work integrates new methodology from the Malleability Module (MaM) of the Proteo framework, further enhancing reconfiguration capabilities with new spawning strategies and data redistribution methods. In this paper, we explore new malleability strategies in HPC dynamic workloads, such as merging MPI communicators and asynchronous reconfigurations, which offer new opportunities for dramatically reducing memory overhead. The proposed enhancements are rigorously evaluated on a world-class supercomputer, demonstrating improved resource utilization and workload efficiency. Results show that dynamic resource management can reduce the workload completion time by 40% and increase the resource utilization by over 20%, compared to static resource allocation.

cs.DC↗

arXiv2020

Compressed Basis GMRES on High Performance GPUs

Krylov methods provide a fast and highly parallel numerical tool for the iterative solution of many large-scale sparse linear systems. To a large extent, the performance of practical realizations of these methods is constrained by the communication bandwidth in all current computer architectures, motivating the recent investigation of sophisticated techniques to avoid, reduce, and/or hide the message-passing costs (in distributed platforms) and the memory accesses (in all architectures). This paper introduces a new communication-reduction strategy for the (Krylov) GMRES solver that advocates for decoupling the storage format (i.e., the data representation in memory) of the orthogonal basis from the arithmetic precision that is employed during the operations with that basis. Given that the execution time of the GMRES solver is largely determined by the memory access, the datatype transforms can be mostly hidden, resulting in the acceleration of the iterative step via a lower volume of bits being retrieved from memory. Together with the special properties of the orthonormal basis (whose elements are all bounded by 1), this paves the road toward the aggressive customization of the storage format, which includes some floating point as well as fixed point formats with little impact on the convergence of the iterative process. We develop a high performance implementation of the "compressed basis GMRES" solver in the Ginkgo sparse linear algebra library and using a large set of test problems from the SuiteSparse matrix collection we demonstrate robustness and performance advantages on a modern NVIDIA V100 GPU of up to 50% over the standard GMRES solver that stores all data in IEEE double precision.

cs.MS↗

arXiv2019

Exploiting nested task-parallelism in the $\mathcal{H}-LU$ factorization

We address the parallelization of the LU factorization of hierarchical matrices ($\mathcal{H}$-matrices) arising from boundary element methods. Our approach exploits task-parallelism via the OmpSs programming model and runtime, which discovers the data-flow parallelism intrinsic to the operation at execution time, via the analysis of data dependencies based on the memory addresses of the tasks' operands. This is especially challenging for $\mathcal{H}$-matrices, as the structures containing the data vary in dimension during the execution. We tackle this issue by decoupling the data structure from that used to detect dependencies. Furthermore, we leverage the support for weak operands and early release of dependencies, recently introduced in OmpSs-2, to accelerate the execution of parallel codes with nested task-parallelism and fine-grain tasks.

cs.MS↗

arXiv2012

Solving Dense Generalized Eigenproblems on Multi-threaded Architectures

We compare two approaches to compute a portion of the spectrum of dense symmetric definite generalized eigenproblems: one is based on the reduction to tridiagonal form, and the other on the Krylov-subspace iteration. Two large-scale applications, arising in molecular dynamics and material science, are employed to investigate the contributions of the application, architecture, and parallelism of the method to the performance of the solvers. The experimental results on a state-of-the-art 8-core platform, equipped with a graphics processing unit (GPU), reveal that in real applications, iterative Krylov-subspace methods can be a competitive approach also for the solution of dense problems.

cs.PF↗