sLASs: A fully automatic auto-tuned linear algebra library based on OpenMP extensions implemented in OmpSs (LASs Library)

Valero-Lara, Pedro; Catalán, Sandra; Martorell, Xavier; Usui, Tetsuzo; Labarta, Jesús

doi:10.1016/j.jpdc.2019.12.002

Cited by 20 publications

(4 citation statements)

References 12 publications

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“…However, some HPC applications show other patterns that can also benefit from a similar approach to reduce overhead. This is the case of applications that expose multiple levels of parallelism, where the outer levels are dynamic and the inner levels are static, e.g., the sLASs linear algebra solver [19], the Specfem3D simulator [6] and the Quantum ESPRESSO material modeling tool [4]. In these cases, where inner TDG can become static after their first execution, benefits similar or even better than the ones shown in Figure 1 can be expected.…”

Section: Motivationmentioning

confidence: 99%

Enhancing OpenMP Tasking Model: Performance and Portability

Royuela

Quiñones

2021

Lecture Notes in Computer Science

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OpenMP, as the de-facto standard programming model in symmetric multiprocessing for HPC, has seen its performance boosted continuously by the community, either through implementation enhancements or specification augmentations. Furthermore, the language has evolved from a prescriptive nature, as defined by the thread-centric model, to a descriptive behavior, as defined by the task-centric model. However, the overhead related to the orchestration of tasks is still relatively high. Applications exploiting very fine-grained parallelism and systems with a large number of cores available might fail on scaling. In this work, we propose to include the concept of Task Dependency Graph (TDG) in the specification by introducing a new clause, named taskgraph, attached to task or target directives. By design, the TDG allows alleviating the overhead associated with the OpenMP tasking model, and it also facilitates linking OpenMP with other programming models that support task parallelism. According to our experiments, a GCC implementation of the taskgraph is able to significantly reduce the execution time of fine-grained task applications and increase their scalability with regard to the number of threads.

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Section: Motivationmentioning

confidence: 99%

Enhancing OpenMP Tasking Model: Performance and Portability

Royuela

Quiñones

2021

Lecture Notes in Computer Science

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“…In [18] authors presented parts of the first prototype of sLaSs library with auto tunable implementations of operations for linear algebra. They used OmpSs with its task based programming model and features such as weak dependencies and regions with the final clause.…”

Section: Openmp Related Framework and Layers For Parallelizationmentioning

confidence: 99%

Assessment of OpenMP Master–Slave Implementations for Selected Irregular Parallel Applications

Czarnul

2021

Electronics

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The paper investigates various implementations of a master–slave paradigm using the popular OpenMP API and relative performance of the former using modern multi-core workstation CPUs. It is assumed that a master partitions available input into a batch of predefined number of data chunks which are then processed in parallel by a set of slaves and the procedure is repeated until all input data has been processed. The paper experimentally assesses performance of six implementations using OpenMP locks, the tasking construct, dynamically partitioned for loop, without and with overlapping merging results and data generation, using the gcc compiler. Two distinct parallel applications are tested, each using the six aforementioned implementations, on two systems representing desktop and worstation environments: one with Intel i7-7700 3.60GHz Kaby Lake CPU and eight logical processors and the other with two Intel Xeon E5-2620 v4 2.10GHz Broadwell CPUs and 32 logical processors. From the application point of view, irregular adaptive quadrature numerical integration, as well as finding a region of interest within an irregular image is tested. Various compute intensities are investigated through setting various computing accuracy per subrange and number of image passes, respectively. Results allow programmers to assess which solution and configuration settings such as the numbers of threads and thread affinities shall be preferred.

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“…We propose and analyze different strategies in order to parallelize the SpMV kernel included in LASs library [18,19,17], which implements the general SpMV (see Equation 1) and operates on an input matrix stored in CSR format [11]. The main challenge we target through the parallelization of this kernel is balancing the computations among the cores in order to attain good performance.…”

Section: Introductionmentioning

confidence: 99%

Towards an Auto-Tuned and Task-Based SpMV (LASs Library)

Catalán

Usui

Toledo

et al. 2020

OpenMP: Portable Multi-Level Parallelism on Modern Systems

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We present a novel approach to parallelize the SpMV kernel included in LASs (Linear Algebra routines on OmpSs) library, after a deep review and analysis of several well-known approaches. LASs is based on OmpSs, a task-based runtime that extends OpenMP directives, providing more flexibility to apply new strategies. Based on tasking and nesting, with the aim of improving the workload imbalance inherent to the SpMV operation, we present a strategy especially useful for highly imbalanced input matrices. In this approach, the number of created tasks is dynamically decided in order to maximize the use of the resources of the platform. Throughout this paper, SpMV behavior depending on the selected strategy (state of the art and proposed strategies) is deeply analyzed, setting in this way the base for a future auto-tunable code that is able to select the most suitable approach depending on the input matrix. The experiments of this work were carried out for a set of 12 matrices from the Suite Sparse Matrix Collection, all of them with different characteristics regarding their sparsity. The experiments of this work were performed on a node of Marenostrum 4 supercomputer (with two sockets Intel Xeon, 24 cores each) and on a node of Dibona cluster (using one ARM ThunderX2 socket with 32 cores). Our tests show that, for Intel Xeon, the best parallelization strategy reduces the execution time of the reference MKL multi-threaded version up to 67%. On ARM ThunderX2, the reduction is up to 56% with respect to the OmpSs parallel reference.

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sLASs: A fully automatic auto-tuned linear algebra library based on OpenMP extensions implemented in OmpSs (LASs Library)

Cited by 20 publications

References 12 publications

Enhancing OpenMP Tasking Model: Performance and Portability

Enhancing OpenMP Tasking Model: Performance and Portability

Assessment of OpenMP Master–Slave Implementations for Selected Irregular Parallel Applications

Towards an Auto-Tuned and Task-Based SpMV (LASs Library)

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