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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.

The optimization of large-scale data sets depends on the technologies and methods used. The MapReduce model, implemented on Apache Hadoop or Spark, allows splitting large data sets into a set of blocks distributed on several machines. Data compression reduces data size and transfer time between disks and memory but requires additional processing. Therefore, finding an optimal tradeoff is a challenge, as a high compression factor may underload Input/Output but overload the processor. The paper aims to present a system enabling the selection of the compression tools and tuning the compression factor to reach the best performance in Apache Hadoop and Spark infrastructures based on simulation analyzes.

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.

Nowadays quantum physics is crucial for several scientific applications, where it is no longer possible to neglect the environmental interaction, like dissipation and decoherence. In these cases, the quantum systems are usually treated as open systems and their time-evolution is described by a density matrix in frames of the master equation, instead of the Hilbert-space vector and the Schrodinger equation. The visualization of such quantum systems allows users to calculate and study the sensitivity of the parameters, like excitation photon numbers or photonnumber distribution functions or Wigner functions. In this paper, a cloud service for numerical calculations and visualization of photonic dissipative systems is presented, which enables numerical simulations and visualizations of a wide variety of Hamiltonians, including those with arbitrary time-dependences widely used in many physics applications. The service allows creating graphics and charts for interacting complex systems and simulating their time evolution with many available timeevolution drivers.