Draško Tomić

Spectral performance evaluation of parallel processing systems

Abstract The aim of this paper is that spectral determinants are objects that can be effectively used as a performance prediction tool for the modern parallel processing systems. In the aim to confirm this we give the matrix representation of the linear evolution operator of the certain class of parallel processing systems. An explicit polynomial expression of the corresponding spectral determinant has been established and eigenvalues were computed. Derived eigenvalues were validated against the results of the simulation. The strong agreement between computed results and those obtained through the simulation has been found.

Exploring bacterial biofilms with NAMD, a highly scalable parallel code for complex molecular simulations

Bacterial biofilms are highly complex structures. We are more and more recognizing that bacterial biofilms are predominant forms of the bacterial existence against the planktonic one. Under certain circumstances, bacteria starts to build biofilm and forms 3D structure, so called extracellular matrix. Hulled within this matrix, bacteria becomes more prone to host defense mechanisms and most antibiotics, thus expressing considerably higher virulence and antibiotic resistance than its planktonic form. Because of their importance, numerous researchers investigated bacterial biofilms in the last decades, using numerous methods, like electron microscopy, mass spectroscopy and nuclear magnetic resonance. However, neither of these methods is able to reveal an exact structure of extracellular matrix. Exploring dynamics of extracellular matrix is even more complex, and out of the reach for known analysis methods. For these reasons, there is a need for more effective method, and this could be computer driven simulation. In order to check if it could be a method of choice, we estimated the computational resources needed to simulate the bacterial biofilm. We found that possibility of performing this simulation in the reasonable time on fastest supercomputers today does not exists, and will not be available until at least 2028. For this reason, we explored possibilities of running NAMD based bacterial biofilms simulations on Cloud, and landed with the same conclusion. Besides, we found that for both approaches NAMD has to extend its scalability from about current 500.000 cores to many millions of cores in the future.

Control and optimization of complex biological systems

Healthy and stable state of complex biological systems lies in the narrow region between chaos and frozen state. The cause of chaos is often small initial change in starting conditions, quickly spreading and leading to system wide changes. For example, few microbial pathogens can quickly lead the host to the serious illness and even death. By the definition, an attractor in a certain dynamical system is a set of physical properties toward which this system tends to evolve, regardless of the starting conditions. It is not easy to control chaos and move system from attractor back to the stable state. This is even more difficult in case of hyperchaos with more attractors. Existing control strategies are passive; modify control parameters and wait until system lends in a future, hopefully stable state. At there is a clear need for more efficient control strategy, we propose the optimization of the matrix representing an evolution operator of the system under the control. Matrix elements are system state functions, and function variables are control parameters. Eventually, optimizing this matrix with respect to some goals can help to drive system back to stability. On the case of Samoyed dog lady attacked by methicillin-resistant and multi-species bacterial pathogens, we were able to identify the chaotic system with at least three attractors; two of them leading the system into the illness, and one driving it back to the healthy state. Consecutively, we established the matrix of its evolution operator and discussed some ways of the matrix optimization, in a hope our approach might help to develop therapies that are more efficient. Not of the less importance, we hope our experience with integrative therapy including standard therapeutics, herbal extracts and homeopathic remedies could help a clinician confronted with a similar case.

Towards new energy efficiency limits of High Performance Clusters

In recent years performance of High Performance Computing Clusters took precedence over their power consumption. However, costs of energy and demand for ecologically acceptable IT solutions are higher than ever before, therefore a need for HPC clusters with acceptable power consumption becomes increasingly important. Consequently, the Green500 list, which takes into account both performance and power consumption of HPC clusters, almost reached the popularity of the Top500 list. Interestingly, the Green500 list is not an opponent to Top500 list; its core idea is to complement the Top500. Therefore, the Top500 list still serves as the basis for the Green500 list, and its numbers regarding measured HPL performance, are a basis for calculating the Green500 list. Indeed, the Green500 is the Top500 list ordered by HPL measured performance per Watt. Rmax numbers gained from High Performance Linpack benchmarks serve as performance input parameters, and total power consumed during execution of HPL on a certain HPC clusters is a power consumption parameter. The critical question remains: how to measure the consumed power correctly? This paper proposes that if it is not possible to measure the consumed power, one can still use maximum power consumption numbers rated from hardware vendors to find at least the lower bound green efficiency of HPC clusters. The main idea behind this approach is that Rmax values found on Top500 list never achieve Rpeak theoretical values, and that even most efficient HPL benchmark can never utilize computing nodes at their maximum. Furthermore by comparing MFLOPS/W results we gained with those found on Green500 list, we noted the excellent efficiency of the new HPC Isabella cluster recently powered on at University Computing Centre in Zagreb, ranking in just behind University of North Carolina KillDevil Top500 super cluster.