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pro vyhledávání: '"Yoonhee Kim"'
Autor:
Yoonhee Kim, Sejin Kim
Publikováno v:
Cluster Computing. 25:597-617
Data centers and cloud environments have recently started providing graphic processing unit (GPU)-based infrastructure services. Actual general purpose GPU (GPGPU) applications have low GPU utilization, unlike GPU-friendly applications. To improve th
Publikováno v:
Cluster Computing. 23:2273-2285
The low capacity of main memory has become a critical issue in the performance of systems. Several memory schemes, utilizing multiple classes of memory devices, are used to mitigate the problem; hiding the small capacity by placing data in proper mem
Autor:
Yoonhee Kim, Jisun Oh
Publikováno v:
Cluster Computing. 23:2219-2234
Graphics Processing Units (GPU) are widely used for high-speed processes in the computational science areas of biology, chemistry, meteorology, etc. and the machine learning areas of image and video analysis. Recently, data centers and cloud companie
Autor:
Sejin Kim, Yoonhee Kim
Publikováno v:
Cluster Computing.
Recently, improving the overall resource utilization through efficient scheduling of applications on graphic processing unit (GPU) clusters has been a concern. Traditional cluster-orchestration platforms providing GPUs exclusively for applications co
Publikováno v:
Cluster Computing. 23:2179-2191
Container based virtualization is an innovative technology that accelerates software development by providing portability and maintainability of applications. Recently, a growing number of workloads such as high performance computing (HPC) and Deep L
Publikováno v:
Cluster Computing. 23:347-357
RDMA is increasingly becoming popular not only in HPC but also in data centers where high throughput and low latency are critical requirements. RDMA supports several types of transports, each of which has different characteristics, so that users can
Autor:
Yoonhee Kim, Seiin Kim
Publikováno v:
APNOMS
The advent of GPGPU (General-Purpose Graphic Processing Unit) containers enlarges opportunities of acceleration and easy-to-use in clouds. However, there is still lack of research on utilizing efficiently GPU resource and managing multiple applicatio
Publikováno v:
APNOMS
Graphics Processing Units (GPUs) are getting popularly utilized for multi-purpose applications in order to enhance highly performed parallelism of computation. As memory virtualization methods in GPU nodes are not efficiently provided to deal with di
Autor:
Yoonhee Kim, Theodora Adufu
Publikováno v:
Scientific Programming, Vol 2018 (2018)
Recent research and production environments are deploying more container technologies for the execution of HPC applications and for reproducing scientific workflows or computing environments. Research works, however, have not accounted for performanc
Autor:
Yoonhee Kim, Jieun Choi
Publikováno v:
Cluster Computing. 20:3537-3549
With the remarkable growth in cloud computing, computing resources can be manipulated on-demand in most scientific fields. This enables scientists to strategically select their experimental environment. Since it is hard to offer cloud resources in ac