StochSoCs: High performance biocomputing simulations for large scale Systems Biology

The stochastic simulation of large-scale biochemical reaction networks is of great importance for systems biology since it enables the study of inherently stochastic biological mechanisms at the whole cell scale. Stochastic Simulation Algorithms (SSA) allow us to simulate the dynamic behavior of complex kinetic models, but their high computational cost makes them very slow for many realistic size problems. We present a pilot service, named WebStoch, developed in the context of our StochSoCs research project, allowing life scientists with no high-performance computing expertise to perform over the internet stochastic simulations of large-scale biological network models described in the SBML standard format. Biomodels submitted to the service are parsed automatically and then placed for parallel execution on distributed worker nodes. The workers are implemented using multi-core and many-core processors, or FPGA accelerators that can handle the simulation of thousands of stochastic repetitions of complex biomodels, with possibly thousands of reactions and interacting species. Using benchmark LCSE biomodels, whose workload can be scaled on demand, we demonstrate linear speedup and more than two orders of magnitude higher throughput than existing serial simulators.Comment: The 2017 International Conference on High Performance Computing & Simulation (HPCS 2017), 8 page

Scalable Biomodels Stochastic Simulation on a GPU

Ο κύριος σκοπός της πτυχιακής εργασίας ήταν η ανάπτυξη εφαρμογής που θα επιταχύνει στοχαστικές προσομοιώσεις βιολογικών συστημάτων χρησιμοποιώντας κάρτες γραφικών (GPU). Η εφαρμογή θα ενσωματωθεί στην πλατφόρμα StochSocs η οποία αναπτύχθηκε στο Εθνικό και Καποδιστριακό Πανεπιστήμιο Αθηνών. Στην προτεινόμενη εφαρμογή έχουμε ενσωματώσει δύο τρόπους παραλληλοποίησης της μεθόδου First Reaction Method του Gillespie (FRM) για κάρτες γραφικών, οι οποίες έχουν σχεδιαστεί για να έχουν την καλύτερη κλιμάκωση σε σχέση με το μέγεθος του βιομοντέλου (αριθμό αντιδράσεων και μοριακών ειδών που αλληλεπιδρούν δυναμικά) και τον αριθμό παράλληλων προσομοιώσεων. Ο πρώτος τρόπος, που αποκαλείται Single Block, σχεδιάστηκε ώστε να μπορεί να τρέξει αποδοτικά μεγάλο αριθμό παράλληλων προσομοιώσεων ανεξαρτήτως του μεγέθους του κινητικού μοντέλου. Ο δεύτερος τρόπος, που λέγεται Multiple Block, βελτιστοποιήθηκε για λίγες παράλληλες προσομοιώσεις πολύ μεγάλων μοντέλων. Τα αποτελέσματα δείχνουν ότι για όλες σχεδόν τις περιπτώσεις υπερτερεί σε επιδόσεις η Single Block παραλληλοποίηση, εκτός αν το μοντέλο είναι πολύ μεγάλο (πάνω από 2Κ αντιδράσεις) και ο αριθμός στοχαστικών προσομοιώσεων μικρός (< 16). H Single Block μέθοδος επιτυγχάνει στην ΤΙΤΑΝ Χ GPU εντυπωσιακές επιδόσεις που ξεπερνούν τα 12 δισεκατομμύρια αντιδράσεις το δευτερόλεπτο (GigaReactions/sec) για μεγάλα κινητικά μοντέλα με περισσότερες από 512 αντιδράσεις.The main goal of this undergraduate thesis was to develop a CUDA application that accelerates the stochastic simulations of large biological reaction networks using Graphics Processing Units (GPU). The application will be incorporated in the StochSoCs platform developed at the National and Kapodistrian University of Athens. The application supports two modes of parallelization of Gillespie's First Reaction Method (FRM) designed to perform best depending on the size of the user's biological network model (number of reactions and species that interact dynamically) and the number of the stochastic simulation repetitions. The first, called Single Block Mode, was designed to handle efficiently multiple simulations in parallel regardless of the model's size. The second one, called Multiple Block Mode, was optimized to perform in parallel a small number of repetitions of very large kinetic models with thousands of reactions. Our results show that for almost all cases, Single Block Mode exhibits superior performance unless if the model is very large (over 2K reactions) and the number of stochastic repetitions very small (<16). The Single Block Mode on the TITAN X GPU achieves an impressive performance that surpasses 12 billion reactions per second (GigaReactions/sec) for large kinetic models with over 512 reactions

A Practical Hardware Implementation of Systemic Computation

It is widely accepted that natural computation, such as brain computation, is far superior to typical computational approaches addressing tasks such as learning and parallel processing. As conventional silicon-based technologies are about to reach their physical limits, researchers have drawn inspiration from nature to found new computational paradigms. Such a newly-conceived paradigm is Systemic Computation (SC). SC is a bio-inspired model of computation. It incorporates natural characteristics and defines a massively parallel non-von Neumann computer architecture that can model natural systems efficiently. This thesis investigates the viability and utility of a Systemic Computation hardware implementation, since prior software-based approaches have proved inadequate in terms of performance and flexibility. This is achieved by addressing three main research challenges regarding the level of support for the natural properties of SC, the design of its implied architecture and methods to make the implementation practical and efficient. Various hardware-based approaches to Natural Computation are reviewed and their compatibility and suitability, with respect to the SC paradigm, is investigated. FPGAs are identified as the most appropriate implementation platform through critical evaluation and the first prototype Hardware Architecture of Systemic computation (HAoS) is presented. HAoS is a novel custom digital design, which takes advantage of the inbuilt parallelism of an FPGA and the highly efficient matching capability of a Ternary Content Addressable Memory. It provides basic processing capabilities in order to minimize time-demanding data transfers, while the optional use of a CPU provides high-level processing support. It is optimized and extended to a practical hardware platform accompanied by a software framework to provide an efficient SC programming solution. The suggested platform is evaluated using three bio-inspired models and analysis shows that it satisfies the research challenges and provides an effective solution in terms of efficiency versus flexibility trade-off