829 research outputs found

    A Power-Aware Framework for Executing Streaming Programs on Networks-on-Chip

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    Nilesh Karavadara, Simon Folie, Michael Zolda, Vu Thien Nga Nguyen, Raimund Kirner, 'A Power-Aware Framework for Executing Streaming Programs on Networks-on-Chip'. Paper presented at the Int'l Workshop on Performance, Power and Predictability of Many-Core Embedded Systems (3PMCES'14), Dresden, Germany, 24-28 March 2014.Software developers are discovering that practices which have successfully served single-core platforms for decades do no longer work for multi-cores. Stream processing is a parallel execution model that is well-suited for architectures with multiple computational elements that are connected by a network. We propose a power-aware streaming execution layer for network-on-chip architectures that addresses the energy constraints of embedded devices. Our proof-of-concept implementation targets the Intel SCC processor, which connects 48 cores via a network-on- chip. We motivate our design decisions and describe the status of our implementation

    Improving MPI Threading Support for Current Hardware Architectures

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    Threading support for Message Passing Interface (MPI) has been defined in the MPI standard for more than twenty years. While many standard-compliance MPI implementations fully support multithreading, the threading support in MPI still cannot provide the optimal performance on the same level as the non-threading environment. The performance disparity leads to low adoption rate from applications, and eventually, lesser interest in optimizing MPI threading support. However, with the current advancement in computation hardware, the number of CPU core per packet is growing drastically. Using shared-memory MPI communication has become more costly. MPI threading without local communication is one of the alternatives and the some interests are shifting back toward threading to MPI.In this work, we investigate different approaches to leverage the power of thread parallelism and tools to help us to raise the multi-threaded MPI performance to reasonable level. We propose a novel multi-threaded MPI benchmark with multiple communication patterns to stress multiple points of the MPI implementation, with the ability to switch between using MPI process and threads for quick comparison between two modes. Enabling the us, and the others MPI developers to stress test their implementation design.We address the interoperability between MPI implementation and threading frameworks by introducing the thread-synchronization object, an object that gives the MPI implementation more control over user-level thread, allowing for more thread utilization in MPI. In our implementation, the synchronization object relieves the lock contention on the internal progress engine and able to achieve up to 7x the performance of the original implementation. Moving forward, we explore the possibility of harnessing the true thread concurrency. We proposed several strategies to address the bottlenecks in MPI implementation. From our evaluation, with our novel threading optimization, we can achieve up to 22x the performance comparing to the legacy MPI designs

    Argobots: A Lightweight Low-Level Threading and Tasking Framework

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    In the past few decades, a number of user-level threading and tasking models have been proposed in the literature to address the shortcomings of OS-level threads, primarily with respect to cost and flexibility. Current state-of-the-art user-level threading and tasking models, however, either are too specific to applications or architectures or are not as powerful or flexible. In this paper, we present Argobots, a lightweight, low-level threading and tasking framework that is designed as a portable and performant substrate for high-level programming models or runtime systems. Argobots offers a carefully designed execution model that balances generality of functionality with providing a rich set of controls to allow specialization by end users or high-level programming models. We describe the design, implementation, and performance characterization of Argobots and present integrations with three high-level models: OpenMP, MPI, and colocated I/O services. Evaluations show that (1) Argobots, while providing richer capabilities, is competitive with existing simpler generic threading runtimes; (2) our OpenMP runtime offers more efficient interoperability capabilities than production OpenMP runtimes do; (3) when MPI interoperates with Argobots instead of Pthreads, it enjoys reduced synchronization costs and better latency-hiding capabilities; and (4) I/O services with Argobots reduce interference with colocated applications while achieving performance competitive with that of a Pthreads approach

    Improving the interoperability between MPI and task-based programming models

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    In this paper we propose an API to pause and resume task execution depending on external events. We leverage this generic API to improve the interoperability between MPI synchronous communication primitives and tasks. When an MPI operation blocks, the task running is paused so that the runtime system can schedule a new task on the core that became idle. Once the MPI operation is completed, the paused task is put again on the runtime system's ready queue. We expose our proposal through a new MPI threading level which we implement through two approaches. The first approach is an MPI wrapper library that works with any MPI implementation by intercepting MPI synchronous calls, implementing them on top of their asynchronous counterparts. In this case, the task-based runtime system is also extended to periodically check for pending MPI operations and resume the corresponding tasks once MPI operations complete. The second approach consists in directly modifying the MPICH runtime system, a well-known implementation of MPI, to directly call the pause/resume API when a synchronous MPI operation blocks and completes, respectively. Our experiments reveal that this proposal not only simplifies the development of hybrid MPI+OpenMP applications that naturally overlap computation and communication phases; it also improves application performance and scalability by removing artificial dependencies across communication tasks.This work has been developed with the support of the European Union Horizon 2020 Programme through both the INTERTWinE project (agreement No. 671602) and the Marie Sk lodowska-Curie grant (agreement No. 749516); the Spanish Government through the Severo Ochoa Program (SEV-2015-0493); the Spanish Ministry of Science and Innovation (TIN2015-65316-P) and the Generalitat de Catalunya (2017-SGR-1414).Peer ReviewedPostprint (author's final draft

    Integrating blocking and non-blocking MPI primitives with task-based programming models

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    In this paper we present the Task-Aware MPI library (TAMPI) that integrates both blocking and non-blocking MPI primitives with task-based programming models. The TAMPI library leverages two new runtime APIs to improve both programmability and performance of hybrid applications. The first API allows to pause and resume the execution of a task depending on external events. This API is used to improve the interoperability between blocking MPI communication primitives and tasks. When an MPI operation executed inside a task blocks, the task running is paused so that the runtime system can schedule a new task on the core that became idle. Once the blocked MPI operation is completed, the paused task is put again on the runtime system’s ready queue, so eventually it will be scheduled again and its execution will be resumed. The second API defers the release of dependencies associated with a task completion until some external events are fulfilled. This API is composed only of two functions, one to bind external events to a running task and another function to notify about the completion of external events previously bound. TAMPI leverages this API to bind non-blocking MPI operations with tasks, deferring the release of their task dependencies until both task execution and all its bound MPI operations are completed. Our experiments reveal that the enhanced features of TAMPI not only simplify the development of hybrid MPI+OpenMP applications that use blocking or non-blocking MPI primitives but they also naturally overlap computation and communication phases, which improves application performance and scalability by removing artificial dependencies across communication tasks.This work has been developed with the support of the European Union H2020 Programme through both the INTERTWinE project (agreement no. 671602) and the Marie Skłodowska-Curie grant (agreement no. 749516); the Spanish Ministry of Economy and Competitiveness through the Severo Ochoa Program (SEV-2015-0493); the Spanish Ministry of Science and Innovation (TIN2015-65316-P) and the Generalitat de Catalunya (2017-SGR1414).Peer ReviewedPostprint (author's final draft
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