Towards dynamic reconfigurable load-balancing for hybrid desktop platforms

Binotto, Alécio Pedro Delazari; Pereira, Carlos Eduardo; Fellner, Dieter W.

doi:10.1109/ipdpsw.2010.5470804

Cited by 15 publications

(11 citation statements)

References 8 publications

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“…Few works gave attention to modeling overheads such as data communication costs [15]. Performance models for GPUs [24], [25] and heterogeneous framework [26], [27] recently have been proposed. These architectural performance models can be very useful to accurately predict the computational time of jobs in a broad set of GPUs.…”

Section: Performance Modelsmentioning

confidence: 99%

Scheduling in Heterogeneous Computing Environments for Proximity Queries

Kim

Lee

et al. 2013

IEEE Trans. Visual. Comput. Graphics

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Abstract-We present a novel, Linear Programming (LP) based scheduling algorithm that exploits heterogeneous multi-core architectures such as CPUs and GPUs to accelerate a wide variety of proximity queries. To represent complicated performance relationships between heterogeneous architectures and different computations of proximity queries, we propose a simple, yet accurate model that measures the expected running time of these computations. Based on this model, we formulate an optimization problem that minimizes the largest time spent on computing resources, and propose a novel, iterative LP-based scheduling algorithm. Since our method is general, we are able to apply our method into various proximity queries used in five different applications that have different characteristics. Our method achieves an order of magnitude performance improvement by using four different GPUs and two hexa-core CPUs over using a hexa-core CPU only. Unlike prior scheduling methods, our method continually improves the performance, as we add more computing resources. Also, our method achieves much higher performance improvement compared with prior methods as heterogeneity of computing resources is increased. Moreover, for one of tested applications, our method achieves even higher performance than a prior parallel method optimized manually for the application. We also show that our method provides results that are close (e.g., 75%) to the performance provided by a conservative upper bound of the ideal throughput. These results demonstrate the efficiency and robustness of our algorithm that have not been achieved by prior methods. In addition, we integrate one of our contributions with a work stealing method. Our version of the work stealing method achieves 18% performance improvement on average over the original work stealing method. This result shows wide applicability of our approach.

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Section: Performance Modelsmentioning

confidence: 99%

Scheduling in Heterogeneous Computing Environments for Proximity Queries

Kim

Lee

et al. 2013

IEEE Trans. Visual. Comput. Graphics

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“…It was followed by [15], where they together observed a modern desktop as a heterogenous cluster composed of the CPU and several asymmetric PUs, like the GPU. Complemented by [16], it proposed a dynamic load-balancing framework for high-level tasks with performance profiling support. The authors of [17] developed a runtime system oriented to abstract the compute kernels for CPU and GPU, ensuring dynamic binary portability, configuration, and compilation over the PUs, but it also does not address scheduling strategies.…”

Section: Related Workmentioning

confidence: 99%

An Effective Dynamic Scheduling Runtime and Tuning System for Heterogeneous Multi and Many-Core Desktop Platforms

Binotto

Pereira

Kuijper

et al. 2011

2011 IEEE International Conference on High Performance Computing and Communications

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A personal computer can be considered as a one-node heterogeneous cluster that simultaneously processes several application tasks. It can be composed by, for example, asymmetric CPU and GPUs. This way, a high-performance heterogeneous platform is built on a desktop for data intensive engineering calculations. In our perspective, a workload distribution over the Processing Units (PUs) plays a key role in such systems. This issue presents challenges since the cost of a task at a PU is non-deterministic and can be affected by parameters not known a priori. This paper presents a context-aware runtime and tuning system based on a compromise between reducing the execution time of engineering applications -due to appropriate dynamic scheduling -and the cost of computing such scheduling applied on a platform composed of CPU and GPUs. Results obtained in experimental case studies are encouraging and a performance gain of 21.77% was achieved in comparison to the static assignment of all tasks to the GPU.

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“…Complemented by [14], it proposed a dynamic load-balancing framework for high-level tasks with performance profiling support. The authors of [15] developed a runtime system oriented to abstract the compute kernels for CPU and GPU, ensuring dynamic binary portability, configuration, and compilation over the PUs, but it also does not address scheduling strategies.…”

Section: Related Workmentioning

confidence: 99%

Effective Dynamic Scheduling on Heterogeneous Multi/Manycore Desktop Platforms

Binotto

Pedras

Goetz

et al. 2010

2010 22nd International Symposium on Computer Architecture and High Performance Computing Workshops

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GPUs (Graphics Processing Units) have become one of the main co-processors that contributed to desktops towards high performance computing. Together with multicore CPUs and other co-processors, a powerful heterogeneous execution platform is built on a desktop for data intensive calculations. In our perspective, we see the modern desktop as a heterogeneous cluster that can deal with several applications' tasks at the same time. To improve application performance and explore such heterogeneity, a distribution of workload over the asymmetric PUs (Processing Units) plays an important role for the system. However, this problem faces challenges since the cost of a task at a PU is non-deterministic and can be influenced by several parameters not known a priori, like the problem size domain. We present a context-aware architecture that maximizes application performance on such platforms. This approach combines a model for a first scheduling based on an offline performance benchmark with a runtime model that keeps track of tasks' real performance. We carried a demonstration using a CPU-GPU platform for computing iterative SLEs (Systems of Linear Equations) solvers using the number of unknowns as the main parameter for assignment decision. We achieved a gain of 38.3% in comparison to the static assignment of all tasks to the GPU (which is done by current programming models, such as OpenCL and CUDA for Nvidia).

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Towards dynamic reconfigurable load-balancing for hybrid desktop platforms

Cited by 15 publications

References 8 publications

Scheduling in Heterogeneous Computing Environments for Proximity Queries

Scheduling in Heterogeneous Computing Environments for Proximity Queries

An Effective Dynamic Scheduling Runtime and Tuning System for Heterogeneous Multi and Many-Core Desktop Platforms

Effective Dynamic Scheduling on Heterogeneous Multi/Manycore Desktop Platforms

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