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 FPGA accelerator of the NRM algorithm for efficient stochastic simulation of large-scale biochemical reaction networks

Stochastic simulation of large-scale biochemical reaction networks, with thousands of reactions, is important for systems biology and medicine since it will enable the insilico experimentation with genome-scale reconstructed networks. FPGA based stochastic simulation accelerators can exploit parallelism, but have been limited on the size of biomodels they can handle. We present a high performance scalable System on Chip architecture for implementing Gibson and Bruck's Next Reaction Method efficiently in reconfigurable hardware. Our MPSoC uses aggressive pipelining at the core level and also combines many cores into a Network on Chip to also execute in parallel stochastic repetitions of complex biomodels, each one with up to 4K reactions. The performance of our NRM core depends only on the average outdegree of the biomodel's Dependencies Graph (DG) and not on the number of DG nodes (reactions). By adding cores to the NoC, the system's performance scales linearly and reaches GCycles/sec levels. We show that a medium size FPGA running at ~200 MHz deliver high speedup gains relative to a popular and efficient software simulator running on a very powerful workstation PC

An FPGA Based Implementation of the Exact Stochastic Simulation Algorithm

Mathematical and statistical modeling of biological systems is a desired goal for many years. Many biochemical models are often evaluated using a deterministic approach, which uses differential equations to describe the chemical interactions. However, such an approach is inaccurate for small species populations as it neglects the discrete representation of population values, presents the possibility of negative populations, and does not represent the stochastic nature of biochemical systems. The Stochastic Simulation Algorithm (SSA) developed by Gillespie is able to properly account for these inherent noise fluctuations. Due to the stochastic nature of the Monte Carlo simulations, large numbers of simulations must be run in order to get accurate statistics for the species populations and reactions. However, the algorithm tends to be computationally heavy and leads to long simulation runtimes for large systems. Therefore, this thesis explores implementing the SSA on a Field Programmable Gate Array (FPGA) to improve performance. Employing the Field programmable Gate Arrays exploits the parallelism present in the SSA, providing speedup over the software implementations that execute sequentially. In contrast to prior work that requires re-construction and re-synthesis of the design to simulate a new biochemical system, this work explores the use of reconfigurable hardware in implementing a generic biochemical simulator

Many-Core CPUs Can Deliver Scalable Performance to Stochastic Simulations of Large-Scale Biochemical Reaction Networks

Stochastic simulation of large-scale biochemical reaction networks is becoming essential for Systems Biology. It enables the in-silico investigation of complex biological system dynamics under different conditions and intervention strategies, while also taking into account the inherent "biological noise" especially present in the low species count regime. It is however a great computational challenge since in practice we need to execute many repetitions of a complex simulation model to assess the average and extreme cases behavior of the dynamical system it represents. The problem's work scales quickly, with the number of repetitions required and the number of reactions in the bio-model. The worst case scenario s when there is a need to run thousands of repetitions of a complex model with thousands of reactions. We have developed a stochastic simulation software framework for many- and multi-core CPUs. It is evaluated using Intel's experimental many-cores Single-chip Cloud Computer (SCC) CPU and the latest generation consumer grade Core i7 multi-core Intel CPU, when running Gillespie's First Reaction Method exact stochastic simulation algorithm. It is shown that emerging many-core NoC processors can provide scalable performance achieving linear speedup as simulation work scales in both dimensions

Accelerating Exact Stochastic Simulation of Biochemical Systems

The ability to accurately and efficiently simulate computer models of biochemical systems is of growing importance to the molecular biology and pharmaceutical research communities. Exact stochastic simulation is a popular approach for simulating such systems because it properly represents genetic noise and it accurately represents systems with small populations of chemical species. Unfortunately, the computational demands of exact stochastic simulation often limit its applicability. To enable next-generation whole-cell and multi-cell stochastic modeling, advanced tools and techniques must be developed to increase simulation efficiency. This work assesses the applicability of a variety of hardware and software acceleration approaches for exact stochastic simulation including serial algorithm improvements, parallel computing, reconfigurable computing, and cluster computing. Through this analysis, improved simulation techniques for biological systems are explored and evaluated

Design of a CMOS-Memristive Mixed-Signal Neuromorphic System with Energy and Area Efficiency in System Level Applications

The von Neumann architecture has been the backbone of modern computers for several years. This computational framework is popular because it defines an easy, simple and cheap design for the processing unit and memory. Unfortunately, this architecture faces a huge bottleneck going forward since complexity in computations now demands increased parallelism and this architecture is not efficient at parallel processing. Moreover, the post-Moore\u27s law era brings a constant demand for energy-efficient computing with fewer resources and less area. Hence, researchers are interested in establishing alternatives to the von Neumann architecture and neuromorphic computing is one of the few aspiring computing architectures that contributes to this research effectively. Initially, neuromorphic computing attracted attention because of the parallelism found in the bio-inspired networks and they were interested in leveraging this advantage on a single chip. Moreover, the need for speed in real time performance also escalated the popularity of neuromorphic computing and different research groups started working on hardware implementations of neural networks. Also, neuroscience is consistently building a better understanding of biological networks that provides opportunities for bridging the gap between biological neuronal activities and artificial neural networks. As a consequence, the idea behind neuromorphic computing has continued to gain in popularity. In this research, a memristive neuromorphic system for improved power and area efficiency has been presented. This particular implementation introduces a mixed-signal platform to implement neural networks in a synchronous way. In addition to mixed-signal design, a nano-scale memristive device has been introduced that provides power and area efficiency for the overall system. The system design also includes synchronous digital long term plasticity (DLTP), an online learning methodology that helps train the neural networks during the operation phase, improving the efficiency in learning when considering power consumption and area overhead. This research also proposes a stochastic neuron design with a sigmoidal firing rate. The design introduces variability in the membrane capacitance to reach different membrane potential leading to a variable stochastic firing rate

Σχεδίαση Aρχιτεκτονικής SoC για τον FRM-SSA

Στην Ενότητα 2 παρουσιάζονται οι στοχαστικές μέθοδοι προσομοίωσης και αλγόριθμοι SSA και FRM-SSA του Gillespie. Στην Ενότητα 3 παρουσιάζονται αναλυτικά οι προδιαγραφές του συστήματος που υλοποιήθηκε, ο βαθμός παραμετροποίησης του καθώς και οι τρόποι λειτουργίας του. Στην Ενότητα 4 αναλύεται η αρχιτεκτονική FRM SoC σε επίπεδο συστήματος καθώς επίσης γίνεται και σύντομη αναφορά στο σύστημα επικοινωνίας υπολογιστή και συστήματος. Στην Ενότητα 5 παρουσιάζεται η αρχιτεκτονική της επεξεργαστικής μονάδας (FRM Processing Unit - FPU) ενός SSA Core. Δίνεται έμφαση στη δίοδο δεδομένων της FPU ενώ περιγράφονται αναλυτικά και οι υπόλοιπες μονάδες που πλαισιώνουν τη δίοδο δεδομένων της FPU. Επιπλέον παρουσιάζεται και η θεωρητική μελέτη των επιδόσεων που έγινε κατά το σχεδιασμό. Στην Ενότητα 6 παρουσιάζονται τα στατιστικά αποτελέσματα που προέκυψαν από τη σύνθεση του συστήματος για διάφορους τρόπους λειτουργίας. Στην 7 και τελευταία ενότητα παρουσιάζονται πραγματικά αποτέλεσμα από δοκιμές του συστήματος με σκοπό την επικύρωση της σχεδίασης. Για αυτό το λόγο γίνεται σύγκριση των αποτελεσμάτων με τα αποτελέσματα γνωστών πλατφόρμων προσομοίωσης

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