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HPC clouds may provide fast access to fully configurable and dynamically scalable virtualized HPC clusters to address the complex and challenging computation and storage-intensive requirements. The complex environmental, software, and hardware requirements and dependencies on such systems make it challenging to carry out our large-scale simulations, prediction systems, and other data and compute-intensive workloads over the cloud. The article aims to present an architecture that enables HPC workloads to be serverless over the cloud (Shoc), one of the most critical cloud capabilities for HPC workloads. On one hand, Shoc utilizes the abstraction power of container technologies like Singularity and Docker, combined with the scheduling and resource management capabilities of Kubernetes. On the other hand, Shoc allows running any CPU-intensive and data-intensive workloads in the cloud without needing to manage HPC infrastructure, complex software, and hardware environment deployments.

High Performance Computing (HPC) accelerates life science discoveries by enabling scientists to analyze large data sets, to develop detailed models of entire biological systems and to simulate complex biological processes. As computational experiments, molecular dynamics simulations are widely used in life sciences to evaluate the equilibrium nature of classical many-body systems The modelling and molecular dynamics study of surfactant, polymer solutions and the stability of proteins and nucleic acids under different conditions, as well as deoxyribonucleic acid proteins are studied. The study aims to understand the scaling behavior of Gromacs (Groningen machine for chemical simulations) on various platforms, and the maximum performance in the prospect of energy consumption that can be accomplished by tuning the hardware and software parameters. Different system sizes (48K, 64K, and 272K) from scientific investigations have been studied show that the GPU (Graphics Processing Unit) scales rather beneficial than other resources, i.e., with GPU support. We track 2-3 times speedup compared to the latest multi-core CPUs. However, the so-called “threading effect” leads to the better results.

Kn¨odel graphs of even order n and degree 1 ≤ δ ≤ log2(n),Wδ,n, are regular graphs that have an underlying topology that is time optimal for algorithms gossiping among n nodes. Because of their distinctive properties, Kn¨odel graphs act as a time-optimal topology for broadcasting and gossiping, thus arising in many settings, including social and communication networks or agent-based modeling simulations. Experimentation, often based on the extensive generation and analysis of complex networks, relies on high-performance computational resources to efficiently simulate the flow of information. The efficacy of such processing commonly depends on parallel processing and proper provisioning of distributed resources. This article aims to introduce a runtime in the partitioned global address space model that is optimized for performance and designed to improve the processing of Kn¨odel graphs. The sequential and parallel generation of synthetic datasets, and simulation of push-based, and broadcast-based gossiping algorithms with detailed analysis of resource usage and runtime have been studied. [ABSTRACT FROM AUTHOR]

The parallel scaling of GROMACS package has been studied in detail by estimation the performance of machine in presence of new generation graphical cards. This paper aims to estimate the speedup and performance issues depending on GROMACS network parametrics and as well as the version. The obtained results show that depending on threads/MPI ratio the new version of GROMACS plays rather good than previous one, meanwhile there are cases when the performance values are nearly same. The presence of GPU is significantly increasing the performance scaling up to ten time acceleration. Pinning threads to cores guaranties up to 15-35% increase. [ABSTRACT FROM AUTHOR]

The most important factor of High performance computing (HPC) systems nowadays is to limit or decrease the power consumption while preserving a high utilization. And with the availability of alternative energy, which powers such systems, there is a need to maximize the usage of alternative energy over brown power. For now, the usage of alternative energy is varying in time due to different factors such as sunny days, the wind, etc. and it is crucial to have an energy-aware algorithm to maximize the usage of this energy. In this paper a SaaS service is presented to optimize a usage of alternative energy, to reduce the power consumption and to preserve a best possible percentage of resource utilization. [ABSTRACT FROM AUTHOR]