Assessing the impact of high-performance computing on the drug discovery and development process

2004 ◽  
Vol 2 (5) ◽  
pp. 175-179 ◽  
Author(s):  
Richard K. Scott
2020 ◽  
Vol 15 (9) ◽  
pp. 981-985
Author(s):  
Savíns Puertas-Martín ◽  
Antonio J. Banegas-Luna ◽  
María Paredes-Ramos ◽  
Juana L. Redondo ◽  
Pilar M. Ortigosa ◽  
...  

2019 ◽  
Vol 27 (3) ◽  
pp. 263-267
Author(s):  
Alexander S. Ayriyan

In this note we discuss the impact of development of architecture and technology of parallel computing on the typical life-cycle of the computational experiment. In particular, it is argued that development and installation of high-performance computing systems is indeed important itself regardless of specific scientific tasks, since the presence of cutting-age HPC systems within an academic infrastructure gives wide possibilities and stimulates new researches.


2019 ◽  
Vol 27 (3) ◽  
pp. 263-267
Author(s):  
Alexander S. Ayriyan

In this note we discuss the impact of development of architecture and technology of parallel computing on the typical life-cycle of the computational experiment. In particular, it is argued that development and installation of high-performance computing systems is indeed important itself regardless of specific scientific tasks, since the presence of cutting-age HPC systems within an academic infrastructure gives wide possibilities and stimulates new researches.


Author(s):  
Ouidad Achahbar ◽  
Mohamed Riduan Abid

The ongoing pervasiveness of Internet access is intensively increasing Big Data production. This, in turn, increases demand on compute power to process this massive data, and thus rendering High Performance Computing (HPC) into a high solicited service. Based on the paradigm of providing computing as a utility, the Cloud is offering user-friendly infrastructures for processing Big Data, e.g., High Performance Computing as a Service (HPCaaS). Still, HPCaaS performance is tightly coupled with the underlying virtualization technique since the latter is responsible for the creation of virtual machines that carry out data processing jobs. In this paper, the authors evaluate the impact of virtualization on HPCaaS. They track HPC performance under different Cloud virtualization platforms, namely KVM and VMware-ESXi, and compare it against physical clusters. Each tested cluster provided different performance trends. Yet, the overall analysis of the findings proved that the selection of virtualization technology can lead to significant improvements when handling HPCaaS.


2016 ◽  
Vol 17 (14) ◽  
pp. 1578-1579
Author(s):  
Horacio Pérez-Sánchez ◽  
Sandra Gesing ◽  
Ivan Merelli

Author(s):  
David Lowther ◽  
Vahid Ghorbanian ◽  
Mohammad Hossain Mohammadi ◽  
Issah Ibrahim

Purpose The design of electromagnetic systems for a variety of applications such as induction heating, electrical machines, actuators and transformers requires the solution of a multi-physics problem often involving thermal, structural and mechanical coupling to the electromagnetic system. This results in a complex analysis system embedded within an optimization process. The appearance of high-performance computing systems over the past few years has made coupled simulations feasible for the design engineer. When coupled with surrogate modelling techniques, it is possible to significantly reduce the wall clock time for generating a complete design while including the impact of the multi-physics performance on the device. Design/methodology/approach An architecture is proposed for linking multiple singe physics analysis tools through the material models and a controller which schedules the execution of the various software tools. The combination of tools is implemented on a series of computational nodes operating in parallel and creating a “super node” cluster within a collection of interconnected processors. Findings The proposed architecture and job scheduling system can allow a parallel exploration of the design space for a device. Originality/value The originality of the work derives from the organization of the parallel computing system into a series of “super nodes” and the creation of a materials database suitable for multi-physics interactions.


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