با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
---|---|---|
1 |
Troodon: A machine-learning based load-balancing application scheduler for CPU–GPU system
Troodon: یک برنامه زمانبندی برنامه تعادل بار بر مبنای یادگیری ماشین برای سیستم CPU-GPU-2019 Heterogeneous computing machines consisting of a CPU and one or more GPUs are increasingly being
used today because of their higher performance-cost ratio and lower energy consumption. To program
such heterogeneous systems, OpenCL has become an industry standard due to the portability across
various computing architectures. To exploit the computing capabilities of heterogeneous systems, application
developers are porting their cluster and Cloud applications using OpenCL. With the increasing
number of such applications, the use of shared accelerating computing devices (such as CPUs and
GPUs) should be managed using an efficient load-balancing scheduling heuristic capable of reducing
execution time, increasing throughput with high device utilization. Mostly, the OpenCL applications
are suited (execute faster) on a specific computing device (CPU or GPU) and with varying data-sizes
the speedup obtained by an application on the suitable device varies too. Applications’ mapping
to computing devices without considering device suitability and obtainable speedup on a suitable
device leads to sub-optimal execution time, lower throughput and load imbalance. Therefore, an
application scheduler should consider both the device-suitability and speedup variation for scheduling
decisions leading to a reduction in execution time and an increase in throughput. In this paper,
we present a novel load-balancing scheduling heuristic named as Troodon that considers machinelearning
based device-suitability model that classify OpenCL applications into either CPU suitable
or GPU suitable. Moreover, a speedup predictor that predicts the amount of speedup that jobs
will obtain when executed on a suitable device is also part of the Troodon. Troodon incorporates
the E-OSched scheduling mechanism to map jobs on CPU and GPUs in a load balanced way. This
results in reduced applications execution time, increased system throughput, and improved device
utilization. We evaluate the proposed scheduler using a large number of data-parallel applications
and compared with several other state-of-the-art scheduling heuristics. The experimental evaluation
has demonstrated that the proposed scheduler outperformed the existing heuristics and reduced the
application execution time up to 38% with increased system throughput and device utilization. Keywords: Heterogeneous system | Scheduling | Device suitability | Load-balancing | Machine learning |
مقاله انگلیسی |