6.1.1 MatchMaker

• Name: Maximum matchings in bipartite graphs
• Keywords: Graph algorithmics, Matching
• Scientific Description: The implementations of ten exact algorithms and four heuristics for solving the problem of finding a maximum cardinality matchings in bipartite graphs are provided.
• Functional Description: This software provides algorithms to solve the maximum cardinality matching problem in bipartite graphs.
• URL: https://gitlab.inria.fr/bora-ucar/matchmaker
• Publications: hal-00786548, hal-00763920
• Contact: Bora Uçar
• Participants: Kamer Kaya, Johannes Langguth

6.1.2 PaStiX

• Name: Parallel Sparse matriX package
• Keywords: Linear algebra, High-performance calculation, Sparse Matrices, Linear Systems Solver, Low-Rank compression
• Scientific Description: PaStiX is based on an efficient static scheduling and memory manager, in order to solve 3D problems with more than 50 million of unknowns. The mapping and scheduling algorithm handle a combination of 1D and 2D block distributions. A dynamic scheduling can also be applied to take care of NUMA architectures while taking into account very precisely the computational costs of the BLAS 3 primitives, the communication costs and the cost of local aggregations.
• Functional Description:

  PaStiX is a scientific library that provides a high performance parallel solver for very large sparse linear systems based on block direct and block ILU(k) methods. It can handle low-rank compression techniques to reduce the computation and the memory complexity. Numerical algorithms are implemented in single or double precision (real or complex) for LLt, LDLt and LU factorization with static pivoting (for non symmetric matrices having a symmetric pattern). The PaStiX library uses the graph partitioning and sparse matrix block ordering packages Scotch or Metis.

  The PaStiX solver is suitable for any heterogeneous parallel/distributed architecture when its performance is predictable, such as clusters of multicore nodes with GPU accelerators or KNL processors. In particular, we provide a high-performance version with a low memory overhead for multicore node architectures, which fully exploits the advantage of shared memory by using an hybrid MPI-thread implementation.

  The solver also provides some low-rank compression methods to reduce the memory footprint and/or the time-to-solution.

• URL: https://gitlab.inria.fr/solverstack/pastix
• Authors: Xavier Lacoste, Pierre Ramet, Mathieu Faverge, Pascal Hénon, Tony Delarue, Esragul Korkmaz, Grégoire Pichon
• Contacts: Pierre Ramet, Mathieu Faverge
• Participants: Tony Delarue, Grégoire Pichon, Mathieu Faverge, Esragul Korkmaz, Pierre Ramet
• Partners: INP Bordeaux, Université de Bordeaux

Resilient scheduling heuristics for rigid parallel jobs

We have focused on the resilient scheduling of parallel jobs on high performance computing (HPC) platforms to minimize the overall completion time, or makespan. We have revisited the problem by assuming that jobs are subject to transient or silent errors, and hence may need to be re-executed each time they fail to complete successfully. This work generalizes the classical framework where jobs are known offline and do not fail: in this classical framework, list scheduling that gives priority to longest jobs is known to be a 3-approximation when imposing to use shelves, and a 2-approximation without this restriction. We show that when jobs can fail, using shelves can be arbitrarily bad, but unrestricted list scheduling remains a 2-approximation. We have designed several heuristics, some list-based and some shelf-based, along with different priority rules and backfilling options. We have assessed and compared their performance through an extensive set of simulations, using both synthetic jobs and log traces from the Mira supercomputer.

This work has obtained the best paper award at the APDCM'2020 conference 17, and an extended version was published in IJNC 9.

Resilient scheduling of moldable jobs to cope with silent errors

We have then focused on the resilient scheduling of moldable parallel jobs on high-performance computing (HPC) platforms. Moldable jobs allow for choosing a processor allocation before execution, and their execution time obeys various speedup models. The objective is to minimize the overall completion time of the jobs, or the makespan, when jobs can fail due to silent errors and hence may need to be re-executed after each failure until successful completion. Our work generalizes the classical scheduling framework for failure-free jobs. To cope with silent errors, we introduce two resilient scheduling algorithms, LPA-List and Batch-List, both of which use the List strategy to schedule the jobs. Without knowing a priori how many times each job will fail, LPA-List relies on a local strategy to allocate processors to the jobs, while Batch-List schedules the jobs in batches and allows only a restricted number of failures per job in each batch. We prove new approximation ratios for the two algorithms under several prominent speedup models (e.g., roofline, communication, Amdahl, power, monotonic, and a mixed model). An extensive set of simulations is conducted to evaluate different variants of the two algorithms, and the results show that they consistently outperform some baseline heuristics. Overall, our best algorithm is within a factor of 1.6 of a lower bound on average over the entire set of experiments, and within a factor of 4.2 in the worst case.

Preliminary results with a subset of speedup models have been published in Cluster 2020 16, and an extended version has been submitted 38.

Detection and correction of floating-point errors in matrix-matrix multiplication

This work compares several fault-tolerance methods for the detection and correction of floating-point errors in matrix-matrix multiplication. These methods include replication, triplication, Algorithm-Based Fault Tolerance (ABFT) and residual checking (RC). Error correction for ABFT can be achieved either by recovering the corrupted entries from the correct data and the checksums by solving a small-size linear system of equations, or by recomputing corrupted coefficients. We show that both approaches can be used for RC. We provide a synthetic presentation of all methods before discussing their pros and cons. We have implemented all these methods with calls to optimized BLAS routines, and we provide performance data for a wide range of failure rates and matrix sizes. In addition, with respect to the literature, this work considers relatively high error rates.

This works has been published in 26. The extended version is available as a research report 52.

Robustness of the Young/Daly formula for stochastic iterative applications

The Young/Daly formula for periodic checkpointing is known to hold for a divisible load application where one can checkpoint at any time-step. In an nutshell, the optimal period is $P_{\mathrm{𝑌𝐷}}=\sqrt{2{\mu }_{P}C}$ where ${\mu }_P$ is the Mean Time Between Failures (MTBF) on the platform, and $C$ is the checkpoint time. This work assesses the accuracy of the formula for applications decomposed into computational iterations where: (i) the duration of an iteration is stochastic, i.e., obeys a probability distribution law $𝒟$ of mean ${\mu }_D$; and (ii) one can checkpoint only at the end of an iteration. We first consider static strategies where checkpoints are taken after a given number of iterations $k$ and provide a closed-form, asymptotically optimal, formula for $k$, valid for any distribution $𝒟$. We then show that using the Young/Daly formula to compute $k$ (as $k·{\mu }_D=P_{\mathrm{𝑌𝐷}}$) is a first order approximation of this formula. We also consider dynamic strategies where one decides to checkpoint at the end of an iteration only if the total amount of work since the last checkpoint exceeds a threshold $w_{th}$, and otherwise proceed to the next iteration. Similarly, we provide a closed-form formula for this threshold and show that $P_{\mathrm{𝑌𝐷}}$ is a first-order approximation of $w_{th}$. Finally, we provide an extensive set of simulations where $𝒟$ is either Uniform, Gamma or truncated Normal, which shows the global accuracy of the Young/Daly formula, even when the distribution $𝒟$ had a large standard deviation (and when one cannot use a first-order approximation). Hence we establish that the relevance of the formula goes well beyond its original framework.

This work has been published in 19. The extended version is available as a research report 42.

7.2.1 Minimizing energy consumption

Energy is a major concern, not only in large-scale computing platform as seen above, but also for embedded and real-time systems. We have conducted several studies to reduce the energy footprint of such platforms, with the additional constraint of ensuring performance and reliabilty bounds.

7.2.2 Optimizing memory usage and data movement

We have continued our work on exploring the tradeoffs between memory usage and performance. In particular, we studied how to partition a tree of tasks and how to dynamically schedule a DAG of tasks on memory-limited platforms.

7.2.3 Scheduling stochastic jobs with budget constraints

We have also focused on the problem of scheduling jobs whose processing time is unknown before the computation, with a budget constraint. We have studied two variants of this problem: (i) when we have to maximize the number of completed jobs before a given deadline and (ii) when such jobs must be processed on a platform with fixed-size reservation.

7.2.4 Scheduling online requests

We have focused on the problem of scheduling requests that arrive over time. In this setting, the classical makespan objective function is no longer relevant, and one should focus on the flow (response time) or stretch metrics.

Improving mapping for sparse direct solvers - a trade-off between data locality and load balancing

In order to express parallelism, parallel sparse direct solvers take advantage of the elimination tree to exhibit tree-shaped task graphs, where nodes represent computational tasks and edges represent data dependencies. One of the pre-processing stages of sparse direct solvers consists of mapping computational resources (processors) to these tasks. The objective is to minimize the factorization time by exhibiting good data locality and load balancing. The proportional mapping technique is a widely used approach to solve this resource-allocation problem. It achieves good data locality by assigning the same processors to large parts of the elimination tree. However, it may limit load balancing in some cases. In this work, we propose a dynamic mapping algorithm based on proportional mapping. This new approach, named Steal, relaxes the data locality criterion to improve load balancing. In order to validate the newly introduced method, we perform extensive experiments on the PaStiX sparse direct solver. It demonstrates that our algorithm enables better static scheduling of the numerical factorization while keeping good data locality.

This work appeared in the proceedings of the EuroPar 2020 conference 21.

Trading performance for memory in sparse direct solvers using low-rank compression

Sparse direct solvers using Block Low-Rank compression have been proven efficient to solve problems arising in many real-life applications. Improving those solvers is crucial for being able to 1) solve larger problems and 2) speed up computations. A main characteristic of a sparse direct solver using low-rank compression is when compression is performed. There are two distinct approaches: (1) all blocks are compressed before starting the factorization, which reduces the memory as much as possible, or (2) each block is compressed as late as possible, which usually leads to better speedup. The objective of this work is to design a composite approach, to speedup computations while staying under a given memory limit. This should allow to solve large problems that cannot be solved with Approach 2 while reducing the execution time compared to Approach 1. We propose a memory-aware strategy where each block can be compressed either at the beginning or as late as possible. We first consider the problem of choosing when to compress each block, under the assumption that all information on blocks is perfectly known, i.e., memory requirement and execution time of a block when compressed or not. We show that this problem is a variant of the NP-complete Knapsack problem, and adapt an existing 2-approximation algorithm for our problem. Unfortunately, the required information on blocks depends on numerical properties and in practice cannot be known in advance. We thus introduce models to estimate those values. Experiments on the PaStiX solver demonstrate that our new approach can achieve an excellent trade-off between memory consumption and computational cost. For instance on matrix Geo1438, Approach 2 uses three times as much memory as Approach 1 while being three times faster. Our new approach leads to an execution time only 30% larger than Approach 2 when given a memory 30% larger than the one needed by Approach 1.

A preliminary version of this work is available in the research report 53.

Using H-Matrices into generic tiled algorithm on top of runtime systems

In this work, we propose an extension of the Chameleon library to operate with hierarchical matrices ($\mathscr{H}$-Matrices) and hierarchical arithmetic, producing efficient solvers for dense linear systems arising in Boundary Element Methods (BEM). Our approach builds upon an open-source $\mathscr{H}$-Matrices library from Airbus, named Hmat-oss, that collects sequential numerical kernels for both hierarchical and low-rank structures; the tiled algorithms and task-parallel decompositions available in Chameleon for the solution of linear systems; and the StarPU runtime system to orchestrate an efficient task-parallel (multi-threaded) execution on a multicore architecture. Using an application producing matrices with features close to real industrial applications, we present shared-memory results that demonstrate a fair level of performance, close to (and sometimes better than) the one offered by a pure $\mathscr{H}$-matrix approach, as proposed by Airbus Hmat proprietary (and non open-source) library. Hence, this combination Chameleon + Hmat-oss proposes the most efficient fully open-source software stack to solve dense compressible linear systems on shared memory architectures (distributed memory is under development).

This work appeared in the proceedings of the PDSEC 2020 workshop of IPDPS 18 and at SIAM PP'20 30.

Tensor operations on distributed-memory platforms with multi-GPU nodes

Many domains of scientific simulation (chemistry, condensed matter physics, data science) increasingly eschew dense tensors for block-sparse tensors, sometimes with additional structure (recursive hierarchy, rank sparsity, etc.). Distributed-memory parallel computation with block-sparse tensorial data is paramount to minimize the time-to-solution (e.g., to study dynamical problems or for real-time analysis) and to accommodate problems of realistic size that are too large to fit into the host/device memory of a single node equipped with accelerators. Unfortunately, computation with such irregular data structures is a poor match to the dominant imperative, bulk-synchronous parallel programming model. In this work, we focus on the critical element of block-sparse tensor algebra, namely binary tensor contraction, and report on an efficient and scalable implementation using the task-focused Parsec runtime. High performance of the block-sparse tensor contraction on the Summit supercomputer is demonstrated for synthetic data as well as for real data involved in electronic structure simulations of unprecedented size.

This work is available as a research report 49 and will appear in the proceedings of IPDPS'21.

Matrix symmetrization and sparse direct solvers

We investigate algorithms for finding column permutations of sparse matrices in order to have large diagonal entries and to have many entries symmetrically positioned around the diagonal. The aim is to improve the memory and running time requirements of a certain class of sparse direct solvers. We propose efficient algorithms for this purpose by combining two existing approaches and demonstrate the effect of our findings in practice using a direct solver. We show improvements in a number of components of the running time of a sparse direct solver with respect to the state of the art on a diverse set of matrices.

This work has appeared in the proceedings of CSC2020 29.

Karp-Sipser based kernels for bipartite graph matching

We consider Karp–Sipser, a well known matching heuristic in the context of data reduction for the maximum cardinality matching problem. We describe an efficient implementation as well as modifications to reduce its time complexity in worst case instances, both in theory and in practical cases. We compare experimentally against its widely used simpler variant and show cases for which the full algorithm yields better performance.

This work appears in the proceedings of ALENEX2020 25.

Combinatorial tiling for sparse neural networks

Sparse deep neural networks (DNNs) emerged as the result of search for networks with less storage and lower computational complexity. The sparse DNN inference is the task of using such trained DNN networks to classify a batch of input data. We propose an efficient, hybrid model- and data-parallel DNN inference using hypergraph models and partitioners. We exploit tiling and weak synchronization to increase cache reuse, hide load imbalance, and hide synchronization costs. Finally, a blocking approach allows application of this new hybrid inference procedure for deep neural networks. We initially experiment using the hybrid tiled inference approach only, using the first five layers of networks from the IEEE HPEC 2019 Graph Challenge, and attain up to 2x speedup versus a data-parallel baseline.

This work appears in the proceedings of 2020 IEEE High Performance Extreme Computing (HPEC), Sep 2020, Waltham, MA, United States, and received an innovation award at the MIT/Amazon/IEEE Graph Challenge held within HPEC 28.

Engineering fast almost optimal algorithms for bipartite graph matching

We consider the maximum cardinality matching problem in bipartite graphs. There are a number of exact, deterministic algorithms for this purpose, whose complexities are high in practice. There are randomized approaches for special classes of bipartite graphs. Random 2-out bipartite graphs, where each vertex chooses two neighbors at random from the other side, form one class for which there is an $O(m+nlogn)$-time Monte Carlo algorithm. Regular bipartite graphs, where all vertices have the same degree, form another class for which there is an expected $O(m+nlogn)$-time Las Vegas algorithm. We investigate these two algorithms and turn them into practical heuristics with randomization. Experimental results show that the heuristics are fast and obtain near optimal matchings. They are also more robust than the state of the art heuristics used in the cardinality matching algorithms, and are generally more useful as initialization routines.

This work appears in the proceedings of ESA 2020 - European Symposium on Algorithms, Sep 2020, Pisa, Italy 27.

Programming strategies for irregular algorithms on the Emu Chick

The Emu Chick prototype implements migratory memory-side processing in a novel hardware system. Rather than transferring large amounts of data across the system interconnect, the Emu Chick moves lightweight thread contexts to near-memory cores before the beginning of each remote memory read. Previous work has characterized the performance of the Chick prototype in terms of memory bandwidth and programming differences from more typical, non-migratory platforms, but there has not yet been an analysis of algorithms on this system. This work evaluates irregular algorithms that could benefit from the lightweight, memory-side processing of the Chick and demonstrates techniques and optimization strategies for achieving performance in sparse matrix-vector multiply operation (SpMV), breadth-first search (BFS), and graph alignment across up to eight distributed nodes encompassing 64 nodelets in the Chick system. We also define and justify relative metrics to compare prototype FPGA-based hardware with established ASIC architectures. The Chick currently supports up to 68x scaling for graph alignment, 80 MTEPS for BFS on balanced graphs, and 50% of measured STREAM bandwidth for SpMV.

This work appears in the journal ACM Transactions on Parallel Computing 14.

Algorithms and data structures for hyperedge queries

In this work 39, we consider the problem of querying the existence of hyperedges in hypergraphs. More formally, we are given a hypergraph, and we need to answer queries of the form “does the following set of vertices form a hyperedge in the given hypergraph?”. Our aim is to set up data structures based on hashing to answer these queries as fast as possible. We propose an adaptation of a well-known perfect hashing approach for the problem at hand. We analyze the space and run time complexity of the proposed approach, and experimentally compare it with the state of the art hashing-based solutions. Experiments demonstrate that the proposed approach has shorter query response time than the other considered alternatives, while having the shortest or the second shortest construction time.

8.1.1 Inria International Labs

8.1.2 Inria Associate Team not involved in an IIL

8.1.3 Inria International Partners

8.2.1 Visits of International Scientists

• Helen Xu, a PhD student from MIT, visited the Roma team starting from February 2020. Because of the COVID pandemic her visit had to be cut short.

8.2.2 Visits to International Teams

8.3.1 FP7 & H2020 Projects

8.3.2 Collaborations in European Programs, except FP7 and H2020

8.4.1 ANR

• ANR Project Solharis (2019-2023), 4 years.

  The ANR Project Solhar was launched in November 2019, for a duration of 48 months. It gathers five academic partners (the HiePACS, Roma, RealOpt, STORM and TADAAM) INRIA project-teams, and CNRS-IRIT) and two industrial partners (CEA/CESTA and Airbus CRT). This project aims at producing scalable methods for direct methods for the solution of sparse linear systems on large scale and heterogeneous computing platforms, based on task-based runtime systems.

  The proposed research is organized along three distinct research thrusts. The first objective deals with the development of scalable linear algebra solvers on task-based runtimes. The second one focuses on the deployement of runtime systems on large-scale heterogeneous platforms. The last one is concerned with scheduling these particular applications on a heterogeneous and large-scale environment.

9.1.1 Scientific Events: Selection

9.1.2 Journal

9.1.3 Leadership within the Scientific Community

• Anne Benoit is elected as chair of IEEE TCPP, the Technical Committee on Parallel Processing (2020-2021). She serves in the steering committees of IPDPS and HCW.
• Yves Robert serves in the steering committee of IPDPS, HCW and HeteroPar.
• Bora Uçar is elected as the secretary of SIAM Activity Group on Applied and Computational Discrete Algorithms (for the period Jan 21 – Dec 22).
• Bora Uçar serves in the steering committee of HiPC (2019–2021)

9.1.4 Scientific Expertise

• Frédéric Vivien is an elected member of the scientific council of the École normale supérieure de Lyon.
• Frédéric Vivien is a member of the scientific council of the IRMIA labex http://labex-irmia.u-strasbg.fr/.

9.1.5 Research Administration

• Frédéric Vivien was the vice-head of the LIP laboratory until December 2020.

9.2.1 Teaching

• Licence: Anne Benoit, Responsible of the L3 students at ENS Lyon, France
• Licence: Anne Benoit, Algorithmique avancée, 48h, L3, ENS Lyon, France
• Master: Anne Benoit, Parallel and Distributed Algorithms and Programs, 42h, M1, ENS Lyon, France
• Master: Loris Marchal, Data-Aware Algorithms, 30h, M2 Informatique Fondamentale, ENS Lyon, France.
• Master: Grégoire Pichon, Compilation / traduction des programmes, 22.5h, M1, Univ. Lyon 1, France
• Master: Grégoire Pichon, Programmation système et temps réel, 27.5h, M1, Univ. Lyon 1, France
• Master: Grégoire Pichon, Réseaux, 12h, M1, Univ. Lyon 1, France
• Licence: Grégoire Pichon, Introduction aux réseaux et au web, 36h, L1, Univ. Lyon 1, France
• Licence: Grégoire Pichon, Système d'exploitation, 25.5h, L2, Univ. Lyon 1, France
• Licence: Grégoire Pichon, Programmation concurrente, 24h, L3, Univ. Lyon 1, France
• Licence: Grégoire Pichon, Réseaux, 24h, L3, Univ. Lyon 1, France
• Master: Yves Robert, Responsible of Master Informatique Fondamentale, ENS Lyon, France
• Licence: Yves Robert, Algorithmique, 48h, L3, ENS Lyon, France
• Licence: Yves Robert, Probabilités, 48h, L3, ENS Lyon, France

9.2.2 Supervision

• PhD defended: Changjiang Gou, “Task scheduling on distributed platforms under memory and energy constraints”, defended on September 25, 2020, funding: China Scholarship Council, supervised by Anne Benoit & Loris Marchal.
• PhD defended: Li Han, “Algorithms for detecting and correcting silent and non-functional errors in scientific workflows”, defended on May 6, 2020, advisors: Yves Robert and Frédéric Vivien.
• PhD interrupted: Aurélie Kong Win Chang, “Techniques de résilience pour l’ordonnancement de workflows sur plates-formes décentralisées (cloud computing) avec contraintes de sécurité”, started in October 2016, funding: ENS Lyon, advisors: Yves Robert, Yves Caniou and Eddy Caron. In December 2020, Aurélie decided to move to Grenoble and start a new thesis in the SPADES team.
• PhD defended: Valentin Le Fèvre, “Resilient scheduling algorithms for large-scale platforms”, defended on June 18, 2020, funding: ENS Lyon, advisors: Anne Benoit and Yves Robert.
• PhD defended: Ioannis Panagiotas, “High performance algorithms for big data graph and hypergraph problems”, defended on October 9, 2020, funding: INRIA, advisor: Bora Uçar.
• PhD defended: Filip Pawlowski, “High performance tensor computations”, defended on December 7, 2020, funding: CIFRE, advisors: Bora Uçar and Albert-Jan Yzelman (Huawei).
• PhD in progress: Yishu Du, “Resilience for numerical methods”, started in December 2019, funding: China Scholarship Council and INRIA, advisors: Yves Robert and Loris Marchal.
• PhD in progress: Yiqin Gao, “Replication Algorithms for Real-time Tasks with Precedence Constraints”, started in October 2018, funding: ENS Lyon, advisors: Yves Robert and Frédéric Vivien.
• PhD in progress: Lucas Perotin, “Fault-tolerant scheduling of parallel jobs”, started in October 2020, funding: ENS Lyon, advisors: Anne Benoit and Yves Robert.
• PhD in progress: Zhiwei Wu, “Energy-aware strategies for periodic scientific workflows under reliability constraints on heterogeneous platforms”, started in October 2020, funding: China Scholarship Council, advisors: Frédéric Vivien, Yves Robert, Li Han (ECNU) and Jing Liu (ECNU).

9.2.3 Juries

• Anne Benoit was a reviewer and a member of the jury for the thesis of Valentin Honoré (October 2020, Université de Bordeaux), and for the thesis of Clément Mommessin (December 2020, Université de Grenoble).
• Loris Marchal is a responsible of the competitive selection of ENS Lyon students for Computer Science, and is thus a member of the jury of this competitive exam.
• Loris Marchal was a reviewer and member of the jury for the thesis of Massinissa Ait Aba, defended in June 2020.
• Yves Robert is a member of the 2020 ACM/IEEE-CS George Michael HPC Fellowship committee, the 2021 IEEE Fellow Committee, and the 2021 IEEE Charles Babbage Award Committee. In 2020 he will chair the ACM/IEEE-CS George Michael HPC Fellowship committee.
• Bora Uçar was a member (opponent) of the Doctorate Committee for Jan-Willem Buurlage, Leiden University, the Netherlands, July 1st, 2020. Title: Real-Time Tomographic Reconstruction, supervised by Joost Batenburg and Rob Bisseling.

9.3.1 Articles and contents

• Anne Benoit was interviewed by Interstices in February 2020 on the subject “Quand des erreurs se produisent dans les supercalculateurs” 55.
• Yves Robert, together with George Bosilca, Aurélien Bouteiller and Thomas Herault, gave a full-day tutorial at SC'20 on Fault-tolerant techniques for HPC and Big Data: theory and practice.

International journals

• 7 articleGabrielG. Bathie, LorisL. Marchal, YvesY. Robert and SamuelS. Thibault. Dynamic DAG Scheduling Under Memory Constraints for Shared-Memory PlatformsInternational Journal of Networking and Computing2020, 1-29
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• 8 articleOlivierO. Beaumont, ThomasT. Lambert, LorisL. Marchal and BastienB. Thomas. Performance Analysis and Optimality Results for Data-Locality Aware Tasks Scheduling with Replicated InputsFuture Generation Computer Systems111October 2020, 582-598
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• 9 article AnneA. Benoit, ValentinV. Le Fèvre, PadmaP. Raghavan, YvesY. Robert and HongyangH. Sun. Resilient Scheduling Heuristics for Rigid Parallel Jobs International Journal of Networking and Computing 2020
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• 10 article YvesY. Caniou, EddyE. Caron, AurélieA. Kong Win Chang and YvesY. Robert. Budget-aware scheduling algorithms for scientific workflows with stochastic task weights on IaaS Cloud platforms * Concurrency and Computation: Practice and Experience 2020
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• 11 articleLouis-ClaudeL.-C. Canon, Aurélie Kong WinA. Chang, YvesY. Robert and FrédéricF. Vivien. Scheduling independent stochastic tasks under deadline and budget constraintsInternational Journal of High Performance Computing Applications342March 2020, 246-264
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• 12 articleLouis-ClaudeL.-C. Canon, LorisL. Marchal, BertrandB. Simon and FrédéricF. Vivien. Online Scheduling of Task Graphs on Heterogeneous PlatformsIEEE Transactions on Parallel and Distributed Systems313March 2020, 721-732
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• 13 articleChangjiangC. Gou, AnneA. Benoit and LorisL. Marchal. Partitioning tree-shaped task graphs for distributed platforms with limited memoryIEEE Transactions on Parallel and Distributed Systems317March 2020, 1533 - 1544
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• 14 articleEricE. Hein, SrinivasS. Eswar, AbdurrahmanA. Yaşar, JiajiaJ. Li, JeffreyJ. Young, ThomasT. Conte, Ümit VÜ. Çatalyürek, RichardR. Vuduc, JasonJ. Riedy and BoraB. Uçar. Programming Strategies for Irregular Algorithms on the Emu ChickACM Transactions on Parallel Computing74October 2020, 1-25
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International peer-reviewed conferences

• 15 inproceedingsGabrielG. Bathie, LorisL. Marchal, YvesY. Robert and SamuelS. Thibault. Revisiting dynamic DAG scheduling under memory constraints for shared-memory platformsIPDPS - 2020 - IEEE International Parallel and Distributed Processing Symposium WorkshopsNew Orleans / Virtual, United StatesMay 2020, 1-10
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• 16 inproceedingsAnneA. Benoit, ValentinV. Le Fèvre, LucasL. Perotin, PadmaP. Raghavan, YvesY. Robert and HongyangH. Sun. Resilient Scheduling of Moldable Jobs on Failure-Prone PlatformsCLUSTER 2020 - IEEE International Conference on Cluster ComputingKobe, JapanSeptember 2020, 1-29
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• 17 inproceedingsAnneA. Benoit, ValentinV. Le Fèvre, PadmaP. Raghavan, YvesY. Robert and HongyangH. Sun. Design and Comparison of Resilient Scheduling Heuristics for Parallel JobsAPDCM 2020 - Workshop on Advances in Parallel and Distributed Computational Models (colocated with IPDPS)New Orleans, LA, United StatesMay 2020, 1-27
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• 18 inproceedingsRocíoR. Carratalá-Sáez, MathieuM. Faverge, GrégoireG. Pichon, GuillaumeG. Sylvand and Enrique SE. Quintana-Ortí. Tiled Algorithms for Efficient Task-Parallel H-Matrix SolversPDSEC 2020 - 21st IEEE International Workshop on Parallel and Distributed Scientific and Engineering ComputingNews Orleans, United StatesMay 2020, 1-10
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• 19 inproceedingsYishuY. Du, LorisL. Marchal, YvesY. Robert and GuillaumeG. Pallez. Robustness of the Young/Daly formula for stochastic iterative applicationsICPP 2020 - 49th International Conference on Parallel ProcessingEdmonton / Virtual, CanadaAugust 2020, 1-11
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• 20 inproceedingsAnaA. Gainaru, BriceB. Goglin, ValentinV. Honoré, GuillaumeG. Pallez, PadmaP. Raghavan, YvesY. Robert and HongyangH. Sun. Reservation and Checkpointing Strategies for Stochastic JobsIPDPS 2020 - 34th IEEE International Parallel and Distributed Processing SymposiumNew Orleans, LA / Virtual, United StatesMay 2020, 1-26
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• 21 inproceedingsChangjiangC. Gou, AliA. Al Zoobi, AnneA. Benoit, MathieuM. Faverge, LorisL. Marchal, GrégoireG. Pichon and PierreP. Ramet. Improving mapping for sparse direct solvers: A trade-off between data locality and load balancingEuroPar 2020 - 26th International European Conference on Parallel and Distributed ComputingWarsaw / Virtual, PolandAugust 2020, 1-16
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• 22 inproceedingsChangjiangC. Gou, AnneA. Benoit, MingsongM. Chen, LorisL. Marchal and TongquanT. Wei. Reliable and energy-aware mapping of streaming series-parallel applications onto hierarchical platformsSBAC-PAD 2020 - IEEE 32nd International Symposium on Computer Architecture and High Performance ComputingPorto, PortugalSeptember 2020, 1-11
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• 23 inproceedingsLiL. Han, Louis-ClaudeL.-C. Canon, JingJ. Liu, YvesY. Robert and FrédéricF. Vivien. Improved energy-aware strategies for periodic real-time tasks under reliability constraintsRTSS 2019 - 40th IEEE Real-Time Systems SymposiumYork, United KingdomFebruary 2020, 1-13
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• 24 inproceedingsLiL. Han, YiqinY. Gao, JingJ. Liu, YvesY. Robert and FrédéricF. Vivien. Energy-aware strategies for reliability-oriented real-time task allocation on heterogeneous platformsICPP 2020 - 49th International Conference on Parallel ProcessingEdmonton Alberta, CanadaAugust 2020, 1-11
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• 25 inproceedingsKamerK. Kaya, JohannesJ. Langguth, IoannisI. Panagiotas and BoraB. Uçar. Karp-Sipser based kernels for bipartite graph matchingALENEX20 - SIAM Symposium on Algorithm Engineering and ExperimentsSalt Lake City, Utah, United StatesJanuary 2020, 1-12
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• 26 inproceedingsValentinV. Le Fèvre, ThomasT. Herault, JulienJ. Langou and YvesY. Robert. A comparison of several fault-tolerance methods for the detection and correction of floating-point errors in matrix-matrix multiplicationResilience 2020 - 12th Workshop on Resiliency in High Performance Computing in Clusters, Clouds, and Grids (colocated with Euro-Par)Warsaw, PolandAugust 2020, 1-14
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• 27 inproceedings IoannisI. Panagiotas and BoraB. Uçar. Engineering fast almost optimal algorithms for bipartite graph matching ESA 2020 - European Symposium on Algorithms Pisa, Italy February 2020
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• 28 inproceedings FilipF. Pawłowski, Rob HR. Bisseling, BoraB. Uçar and Albert-Jan NA.-J. Yzelman. Combinatorial Tiling for Sparse Neural Networks 2020 IEEE High Performance Extreme Computing (virtual conference) Waltham, MA, United States September 2020
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• 29 inproceedingsRalucaR. Portase and BoraB. Uçar. Matrix symmetrization and sparse direct solversCSC 2020 - SIAM Workshop on Combinatorial Scientific ComputingSeattle, United States2020, 1-10
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Conferences without proceedings

• 30 inproceedings RocíoR. Carratalá-Sáez, MathieuM. Faverge, GrégoireG. Pichon, Enrique SalvadorE. Quintana-Ortí and GuillaumeG. Sylvand. Exploiting Generic Tiled Algorithms Toward Scalable H-Matrices Factorizations on Top of Runtime Systems SIAM PP20 - SIAM Conference on Parallel Processing for Scientific Computing Seattle, United States February 2020
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Doctoral dissertations and habilitation theses

• 31 thesis ChangjiangC. Gou. Task Mapping and Load-balancing for Performance, Memory, Reliability and Energy Université de Lyon; East China normal university (Shanghai) September 2020
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• 32 thesis LiL. Han. Fault-tolerant and energy-aware algorithms for workflows and real-time systems Université de Lyon; East China normal university (Shanghai) May 2020
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• 33 thesis ValentinV. Le Fèvre. Resilient scheduling algorithms for large-scale platforms Université de Lyon June 2020
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• 34 thesis IoannisI. Panagiotas. On matchings and related problems in graphs, hypergraphs, and doubly stochastic matrices Université de Lyon October 2020
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• 35 thesis Filip igorF. Pawlowski. High-performance dense tensor and sparse matrix kernels for machine learning Université de Lyon December 2020
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Reports & preprints

• 36 report GabrielG. Bathie, LorisL. Marchal, YvesY. Robert and SamuelS. Thibault. Revisiting dynamic DAG scheduling under memory constraints for shared-memory platforms Inria February 2020
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• 37 reportAnneA. Benoit, RedouaneR. Elghazi and YvesY. Robert. Max-stretch minimization on an edge-cloud platformInria - Research Centre Grenoble – Rhône-AlpesOctober 2020, 37
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• 38 report AnneA. Benoit, ValentinV. Le Fèvre, LucasL. Perotin, PadmaP. Raghavan, YvesY. Robert and HongyangH. Sun. Resilient Scheduling of Moldable Parallel Jobs to Cope with Silent Errors Inria - Research Centre Grenoble – Rhône-Alpes January 2021
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• 39 reportJulesJ. Bertrand, FannyF. Dufossé and BoraB. Uçar. Algorithms and data structures for hyperedge queriesInria Grenoble Rhône-AlpesFebruary 2021, 21
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• 40 report RocíoR. Carratalá-Sáez, MathieuM. Faverge, GrégoireG. Pichon, GuillaumeG. Sylvand and Enrique SE. Quintana-Ortí. Tiled Algorithms for Efficient Task-Parallel H-Matrix Solvers Inria February 2020
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• 41 report YishuY. Du, LorisL. Marchal, GuillaumeG. Pallez and YvesY. Robert. Optimal Checkpointing Strategies for Iterative Applications Inria - Research Centre Grenoble – Rhône-Alpes October 2020
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• 42 report YishuY. Du, LorisL. Marchal, GuillaumeG. Pallez and YvesY. Robert. Robustness of the Young/Daly formula for stochastic iterative applications Inria Grenoble Rhône-Alpes March 2020
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• 43 report YiqinY. Gao, YvesY. Robert and FrédéricF. Vivien. Resource-Constrained Scheduling of Stochastic Tasks With Unknown Probability Distribution Inria - Research Centre Grenoble – Rhône-Alpes November 2020
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• 44 report MaximeM. Gonthier, LorisL. Marchal and SamuelS. Thibault. Locality-Aware Scheduling of Independant Tasks for Runtime Systems Inria 2021
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• 45 reportChangjiangC. Gou, AliA. Al Zoobi, AnneA. Benoit, MathieuM. Faverge, LorisL. Marchal, GrégoireG. Pichon and PierreP. Ramet. Improving mapping for sparse direct solvers: A trade-off between data locality and load balancingInria Rhône-AlpesFebruary 2020, 21
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• 46 report ChangjiangC. Gou, AnneA. Benoit, MingsongM. Chen, LorisL. Marchal and TongquanT. Wei. Reliable and energy-aware mapping of streaming series-parallel applications onto hierarchical platforms INRIA June 2020
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• 47 report LiL. Han, YiqinY. Gao, JingJ. Liu, YvesY. Robert and FrédéricF. Vivien. Energy-aware strategies for reliability-oriented real-time task allocation on heterogeneous platforms Univ Lyon, EnsL, UCBL, CNRS, Inria, LIP March 2020
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• 48 report ThomasT. Herault, YvesY. Robert, GeorgeG. Bosilca, RobertR. Harrison, Cannada AC. Lewis and Edward FE. Valeev. Distributed-memory multi-GPU block-sparse tensor contraction for electronic structure Inria - Research Centre Grenoble – Rhône-Alpes June 2020
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• 49 reportThomasT. Herault, YvesY. Robert, GeorgeG. Bosilca, Robert JR. Harrison, Cannada AC. Lewis, Edward FE. Valeev and Jack JJ. Dongarra. Distributed-memory multi-GPU block-sparse tensor contraction for electronic structure (revised version)Inria - Research Centre Grenoble – Rhône-AlpesOctober 2020, 34
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• 50 report AurélieA. Kong Win Chang, YvesY. Caniou, EddyE. Caron and YvesY. Robert. Budget-aware workflow scheduling with DIET Inria Grenoble Rhône-Alpes December 2020
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• 51 reportEsragulE. Korkmaz, MathieuM. Faverge, GrégoireG. Pichon and PierreP. Ramet. Deciding Non-Compressible Blocks in Sparse Direct Solvers using Incomplete FactorizationInria Bordeaux - Sud Ouest2021, 16
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• 52 report ValentinV. Le Fèvre, ThomasT. Herault, JulienJ. Langou and YvesY. Robert. A comparison of several fault-tolerance methods for the detection and correction of floating-point errors in matrix-matrix multiplication Inria - Research Centre Grenoble – Rhône-Alpes June 2020
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• 53 report LorisL. Marchal, ThibaultT. Marette, GrégoireG. Pichon and FrédéricF. Vivien. Trading Performance for Memory in Sparse Direct Solvers using Low-rank Compression INRIA October 2020
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• 54 misc Sonia BenS. Mokhtar, Louis-ClaudeL.-C. Canon, AnthonyA. Dugois, LorisL. Marchal and EtienneE. Rivière. Taming Tail Latency in Key-Value Stores: a Scheduling Perspective (extended version) February 2021
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Scientific popularization

• 55 article A. Benoit and J. Jongwane. Quand des erreurs se produisent dans les supercalculateurs Interstices February 2020
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