ACOTES project: Advanced compiler technologies for embedded streaming

Streaming applications are built of data-driven, computational components, consuming and producing unbounded data streams. Streaming oriented systems have become dominant in a wide range of domains, including embedded applications and DSPs. However, programming efficiently for streaming architectures is a challenging task, having to carefully partition the computation and map it to processes in a way that best matches the underlying streaming architecture, taking into account the distributed resources (memory, processing, real-time requirements) and communication overheads (processing and delay). These challenges have led to a number of suggested solutions, whose goal is to improve the programmer’s productivity in developing applications that process massive streams of data on programmable, parallel embedded architectures. StreamIt is one such example. Another more recent approach is that developed by the ACOTES project (Advanced Compiler Technologies for Embedded Streaming). The ACOTES approach for streaming applications consists of compiler-assisted mapping of streaming tasks to highly parallel systems in order to maximize cost-effectiveness, both in terms of energy and in terms of design effort. The analysis and transformation techniques automate large parts of the partitioning and mapping process, based on the properties of the application domain, on the quantitative information about the target systems, and on programmer directives. This paper presents the outcomes of the ACOTES project, a 3-year collaborative work of industrial (NXP, ST, IBM, Silicon Hive, NOKIA) and academic (UPC, INRIA, MINES ParisTech) partners, and advocates the use of Advanced Compiler Technologies that we developed to support Embedded Streaming.Peer ReviewedPostprint (published version

SCALO: Scalability-Aware Parallelism Orchestration for Multi-Threaded Workloads

This article contributes a solution to orchestrate concurrent application execution to increase throughput. SCALO monitors co-executing applications at runtime to evaluate their scalability

Asynchronous Runtime with Distributed Manager for Task-based Programming Models

Parallel task-based programming models, like OpenMP, allow application developers to easily create a parallel version of their sequential codes. The standard OpenMP 4.0 introduced the possibility of describing a set of data dependences per task that the runtime uses to order the tasks execution. This order is calculated using shared graphs, which are updated by all threads in exclusive access using synchronization mechanisms (locks) to ensure the dependence management correctness. The contention in the access to these structures becomes critical in many-core systems because several threads may be wasting computation resources waiting their turn. This paper proposes an asynchronous management of the runtime structures, like task dependence graphs, suitable for task-based programming model runtimes. In such organization, the threads request actions to the runtime instead of doing them directly. The requests are then handled by a distributed runtime manager (DDAST) which does not require dedicated resources. Instead, the manager uses the idle threads to modify the runtime structures. The paper also presents an implementation, analysis and performance evaluation of such runtime organization. The performance results show that the proposed asynchronous organization outperforms the speedup obtained by the original runtime for different benchmarks and different many-core architectures.Comment: 2020 Parallel Computin

Reducing the burden of parallel loop schedulers for many-core processors

Funder: FP7 People: Marie‐Curie Actions; Id: http://dx.doi.org/10.13039/100011264; Grant(s): 327744Summary: As core counts in processors increases, it becomes harder to schedule and distribute work in a timely and scalable manner. This article enhances the scalability of parallel loop schedulers by specializing schedulers for fine‐grain loops. We propose a low‐overhead work distribution mechanism for a static scheduler that uses no atomic operations. We integrate our static scheduler with the Intel OpenMP and Cilkplus parallel task schedulers to build hybrid schedulers. Compiler support enables efficient reductions for Cilk, without changing the programming interface of Cilk reducers. Detailed, quantitative measurements demonstrate that our techniques achieve scalable performance on a 48‐core machine and the scheduling overhead is 43% lower than Intel OpenMP and 12.1× lower than Cilk. We demonstrate consistent performance improvements on a range of HPC and data analytics codes. Performance gains are more important as loops become finer‐grain and thread counts increase. We observe consistently 16%–30% speedup on 48 threads, with a peak of 2.8× speedup

Load Balancing Regular Meshes on SMPS with MPI

Domain decomposition for regular meshes on parallel computers has traditionally been performed by attempting to exactly partition the work among the available processors (now cores). However, these strategies often do not consider the inherent system noise which can hinder MPI application scalability to emerging peta-scale machines with 10000+ nodes. In this work, we suggest a solution that uses a tunable hybrid static/dynamic scheduling strategy that can be incorporated into current MPI implementations of mesh codes. By applying this strategy to a 3D jacobi algorithm, we achieve performance gains of at least 16% for 64 SMP nodes

Programming parallel dense matrix factorizations with look-ahead and OpenMP

[EN] We investigate a parallelization strategy for dense matrix factorization (DMF) algorithms, using OpenMP, that departs from the legacy (or conventional) solution, which simply extracts concurrency from a multi-threaded version of basic linear algebra subroutines (BLAS). The proposed approach is also different from the more sophisticated runtime-based implementations, which decompose the operation into tasks and identify dependencies via directives and runtime support. Instead, our strategy attains high performance by explicitly embedding a static look-ahead technique into the DMF code, in order to overcome the performance bottleneck of the panel factorization, and realizing the trailing update via a cache-aware multi-threaded implementation of the BLAS. Although the parallel algorithms are specified with a high level of abstraction, the actual implementation can be easily derived from them, paving the road to deriving a high performance implementation of a considerable fraction of linear algebra package (LAPACK) functionality on any multicore platform with an OpenMP-like runtime.The researchers from Universidad Jaume I were supported by the CICYT Projects TIN2014-53495-R and TIN2017-82972-R of the MINECO and FEDER, and the H2020 EU FETHPC Project 671602 "INTERTWinE". The researchers from Universidad Complutense de Madrid were supported by the CICYT Project TIN2015-65277-R of the MINECO and FEDER. Sandra Catalan was supported during part of this time by the FPU program of the Ministerio de Educacion, Cultura y Deporte. Adrian Castello was supported by the ValI+D 2015 FPI program of the Generalitat Valenciana.Catalán, S.; Castelló, A.; Igual, FD.; Rodríguez-Sánchez, R.; Quintana Ortí, ES. (2020). 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In: Proceedings of the IEEE International European Conference on Parallel and Distributed Computing, Santiago de Compostela, Spain (August 2017)Castelló, A., Seo, S., Mayo, R., Balaji, P., Quintana-Ortí, E.S., Peña, A.J.: GLTO: on the adequacy of lightweight thread approaches for OpenMP implementations. In: Proceedings of the International Conference on Parallel Processing, Bristol, UK (August 2017)Catalán, S, Herrero, JR., Quintana-Ortí, E.S., Rodríguez-Sánchez, R., van de Geijn, R.A.: A case for malleable thread-level linear algebra libraries: The LU factorization with partial pivoting. CoRR (2016) arXiv:1611.06365Catalán, S., Igual, F.D., Mayo, R., Rguez-Sánchez, R.: Architecture-aware configuration and scheduling of matrix multiplication on asymmetric multicore processors. Clust. Comput. 19(3), 1037–1051 (2016)Chameleon project. http://project.inria.fr/chameleon/Demmel, J.: Applied Numerical Linear Algebra. 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Studies on automatic parallelization for heterogeneous and homogeneous multicore processors

制度:新 ; 報告番号:甲3537号 ; 学位の種類:博士(工学) ; 授与年月日:2012/2/25 ; 早大学位記番号:新587

FastFlow: Efficient Parallel Streaming Applications on Multi-core

Shared memory multiprocessors come back to popularity thanks to rapid spreading of commodity multi-core architectures. As ever, shared memory programs are fairly easy to write and quite hard to optimise; providing multi-core programmers with optimising tools and programming frameworks is a nowadays challenge. Few efforts have been done to support effective streaming applications on these architectures. In this paper we introduce FastFlow, a low-level programming framework based on lock-free queues explicitly designed to support high-level languages for streaming applications. We compare FastFlow with state-of-the-art programming frameworks such as Cilk, OpenMP, and Intel TBB. We experimentally demonstrate that FastFlow is always more efficient than all of them in a set of micro-benchmarks and on a real world application; the speedup edge of FastFlow over other solutions might be bold for fine grain tasks, as an example +35% on OpenMP, +226% on Cilk, +96% on TBB for the alignment of protein P01111 against UniProt DB using Smith-Waterman algorithm.Comment: 23 pages + cove