Simplifying programming and load balancing of data parallel applications on heterogeneous systems

Pérez, Borja; Bosque, José Luis; Beivide, Ramón

doi:10.1145/2884045.2884051

Cited by 37 publications

(27 citation statements)

References 14 publications

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“…The EngineCL architecture allows to easily incorporate a set of schedulers, as it is shown in Figure 4. In this paper, three well-known schedulers are implemented in EngineCL [20,18,19]. The programmer can select one scheduler per kernel execution, depending on the characteristics and knowledge he has of the problem, data communication and architecture.…”

Section: Schedulersmentioning

confidence: 99%

EngineCL: Usability and Performance in Heterogeneous Computing

Nozal

Bosque

Beivide

2020

Future Generation Computer Systems

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Heterogeneous systems have become one of the most common architectures today, thanks to their excellent performance and energy consumption. However, due to their heterogeneity they are very complex to program and even more to achieve performance portability on different devices. This paper presents EngineCL, a new OpenCL-based runtime system that outstandingly simplifies the co-execution of a single massive data-parallel kernel on all the devices of a heterogeneous system. It performs a set of low level tasks regarding the management of devices, their disjoint memory spaces and scheduling the workload between the system devices while providing a layered API. EngineCL has been validated in two compute nodes (HPC and commodity system), that combine six devices with different architectures. Experimental results show that it has excellent usability compared with OpenCL; a maximum 2.8% of overhead compared to the native version under loads of less than a second of execution and a tendency towards zero for longer execution times; and it can reach an average efficiency of 0.89 when balancing the load.

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Section: Schedulersmentioning

confidence: 99%

EngineCL: Usability and Performance in Heterogeneous Computing

Nozal

Bosque

Beivide

2020

Future Generation Computer Systems

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“…It is limited, nevertheless, to kernels whose relative performance for the small, initial chunks of work-groups may lead to a good prediction of performance for larger chunks. In [8], the authors adapted the OpenMP guided scheduling to partition OpenCL kernels. However this approach does not take into account the data transfers induced by the partitioning of multiple dependent kernels.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Partitioning for Iterated Sequences of Irregular OpenCL Kernels

Huchant

Barthou

Counilh

2018

2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

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OpenCL defines a common parallel programming language for all devices, although writing tasks adapted to the devices, managing communication and load-balancing issues are left to the programmer. We propose in this paper a static/dynamic approach for the execution of an iterated sequence of datadependent kernels on a multi-device heterogeneous architecture. The method allows to automatically distribute irregular kernels onto multiple devices and tackles, without training, both load balancing and data transfers issues coming from hardware heterogeneity, load imbalance within the application itself and load variations between repeated executions of the sequence.

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“…[19]). An interesting example of abstraction built on top of OpenCL is the Maat library [16]. It provides a unified context with an abstract view regardless of the number and nature of devices, for GPU and CPU platforms.…”

Section: Related Workmentioning

confidence: 99%

Multi-device Controllers: A Library to Simplify Parallel Heterogeneous Programming

Moreton-Fernandez

González-Escribano

Llanos

2017

Int J Parallel Prog

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Current HPC clusters are composed by several machines with different computation capabilities and different kinds and families of accelerators. Programming efficiently for these heterogeneous systems has become an important challenge. There are many proposals to simplify the programming and management of accelerator devices, and the hybrid programming, mixing accelerators and CPU cores. However, the portability compromises in many cases the efficiency on different devices, and there are details about the coordination of different types of devices that should be still tackled by the programmer.In this work we introduce the Multi-Controler (MCtrl), an abstract entity implemented in a library, that coordinates the management of heterogeneous devices, including accelerators with different capabilities and sets of CPUcores. Our proposal improves state-of-the-art solutions, simplifying the data partition, mapping, and transparent deployment of both, simple generic kernels portable across different device types, and specialized implementations defined and optimized using specific native or vendor programming models (such as CUDA for NVIDIA's GPUs, or OpenMP for CPU-cores). The runtime system automatically selects and deploys the most appropriate implementation of each kernel for each device, managing the data movements, and hiding the launching details. Results of an experimental study with five study cases indicates that our abstraction allows the development of flexible and high efficient programs, that adapt to the heterogeneous environment.

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Simplifying programming and load balancing of data parallel applications on heterogeneous systems

Cited by 37 publications

References 14 publications

EngineCL: Usability and Performance in Heterogeneous Computing

EngineCL: Usability and Performance in Heterogeneous Computing

Adaptive Partitioning for Iterated Sequences of Irregular OpenCL Kernels

Multi-device Controllers: A Library to Simplify Parallel Heterogeneous Programming

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