scholarly journals Reaching new peaks for the future of the CMS HTCondor Global Pool

2021 ◽  
Vol 251 ◽  
pp. 02055
Author(s):  
A. Pérez-Calero Yzquierdo ◽  
M. Mascheroni ◽  
M. Acosta Flechas ◽  
J. Dost ◽  
S. Haleem ◽  
...  
Keyword(s):  
Data Processing ◽  
Resource Utilization ◽  
Massive Data ◽  
High Luminosity ◽  
Computing Power ◽  
Cms Experiment ◽  
Workload Scheduling ◽  
Distributed Computing Infrastructure ◽  
Computing Infrastructure ◽  
Hpc Cloud

The CMS experiment at CERN employs a distributed computing infrastructure to satisfy its data processing and simulation needs. The CMS Submission Infrastructure team manages a dynamic HTCondor pool, aggregating mainly Grid clusters worldwide, but also HPC, Cloud and opportunistic resources. This CMS Global Pool, which currently involves over 70 computing sites worldwide and peaks at 350k CPU cores, is employed to successfully manage the simultaneous execution of up to 150k tasks. While the present infrastructure is sufficient to harness the current computing power scales, CMS latest estimates predict a noticeable expansion in the amount of CPU that will be required in order to cope with the massive data increase of the High-Luminosity LHC (HL-LHC) era, planned to start in 2027. This contribution presents the latest results of the CMS Submission Infrastructure team in exploring and expanding the scalability reach of our Global Pool, in order to preventively detect and overcome any barriers in relation to the HL-LHC goals, while maintaining high effciency in our workload scheduling and resource utilization.

scholarly journals Analyzing and Estimating the Performance of Concurrent Kernels Execution on GPUs

2017 ◽  
Author(s):  
Rommel Cruz ◽  
Lucia Drummond ◽  
Esteban Clua ◽  
Cristiana Bentes
Keyword(s):  
Resource Utilization ◽  
Real World ◽  
Power Efficiency ◽  
Necessary Conditions ◽  
Performance Degradation ◽  
Computing Power ◽  
Concurrent Execution ◽  
Depth Study ◽  
New Generation

GPUs have established a new baseline for power efficiency and computing power, delivering larger bandwidth and more computing units in each new generation. Modern GPUs support the concurrent execution of kernels to maximize resource utilization, allowing other kernels to better exploit idle resources. However, the decision on the simultaneous execution of different kernels is made by the hardware, and sometimes GPUs do not allow the execution of blocks from other kernels, even with the availability of resources. In this work, we present an in-depth study on the simultaneous execution of kernels on the GPU. We present the necessary conditions for executing kernels simultaneously, we define the factors that influence competition, and describe a model that can determine performance degradation. Finally, we validate the model using synthetic and real-world kernels with different computation and memory requirements.

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