Multicore execution of dynamic dataflow programs on the distributed application layer

Nylanden

et al. 2018

J Sign Process Syst

The increasing number of cores in System on Chips (SoC) has introduced challenges in software parallelization. As an answer to this, the dataflow programming model offers a concurrent and reusability promoting approach for describing applications. In this work, a runtime for executing Dataflow Process Networks (DPN) on multicore platforms is proposed. The main difference between this work and existing methods is letting the operating system perform Central processing unit (CPU) load-balancing freely, instead of limiting thread migration between processing cores through CPU affinity. The proposed runtime is benchmarked on desktop and server multicore platforms using five different applications from video coding and telecommunication domains. The results show that the proposed method offers significant improvements over the stateof-art, in terms of performance and reliability.

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Toward Efficient Execution of RVC-CAL Dataflow Programs on Multicore Platforms

Hautala

Nylanden

et al. 2018

J Sign Process Syst

“…The algorithm is used in wireless communications to mitigate transceiver impairments, and to a great extent consists of parallel 10-tap FIR filters. Functionally, the filter is identical to the one presented in our previous work [22], but the actor descriptions have been rewritten for the proposed framework.…”

Section: Dynamic Predistortion Filteringmentioning

confidence: 99%

Executing Dynamic Data Rate Actor Networks on OpenCL Platforms

2016 IEEE International Workshop on Signal Processing Systems (SiPS)

Hautala

2016

Self Cite

Heterogeneous computing platforms consisting of general purpose processors (GPPs) and graphics processing units (GPUs) have become commonplace in personal mobile devices and embedded systems. For years, programming of these platforms was very tedious and simultaneous use of all available GPP and GPU resources required low-level programming to ensure efficient synchronization and data transfer between processors. However, in the last few years several high-level programming frameworks have emerged, which enable programmers to describe applications by means of abstractions such as dataflow or Kahn process networks and leave parallel execution, data transfer and synchronization to be handled by the framework.Unfortunately, even the most advanced high-level programming frameworks have had shortcomings that limit their applicability to certain classes of applications. This paper presents a new, dataflow-flavored programming framework targeting heterogeneous platforms, and differs from previous approaches by allowing GPUmapped actors to have data dependent consumption of inputs / production of outputs. Such flexibility is essential for configurable and adaptive applications that are becoming increasingly common in signal processing. In our experiments it is shown that this feature allows up to 5× increase in application throughput.The proposed framework is validated by application examples from the video processing and wireless communications domains. In the experiments the framework is compared to a well-known reference framework and it is shown that the proposed framework enables both a higher degree of flexibility and better throughput.

Design Flow for GPU and Multicore Execution of Dynamic Dataflow Programs

Nylanden

2017

J Sign Process Syst

Self Cite

Dataflow programming has received increasing attention in the age of multicore and heterogeneous computing. Modular and concurrent dataflow program descriptions enable highly automated approaches for design space exploration, optimization and deployment of applications. A great advance in dataflow programming has been the recent introduction of the RVC-CAL language. Having been standardized by the ISO, the RVC-CAL dataflow language provides a solid basis for the development of tools, design methodologies and design flows. This paper proposes a novel design flow for mapping RVC-CAL dataflow programs to parallel and heterogeneous execution platforms. Through the proposed design flow the programmer can describe an application in the RVC-CAL language and map it to multi-and many-core platforms, as well as GPUs, for efficient execution. The functionality and efficiency of the proposed approach is demonstrated by a parallel implementation of a video processing application and a run-time reconfigurable filter for telecommunications. Experiments are performed on GPU and multicore platforms with up to 16 cores, and the results show that for high-performance applications the proposed design flow provides up to 4× higher throughput than the state-ofthe-art approach in multicore execution of RVC-CAL programs.