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

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

Biomolecular System Design: Architecture, Synthesis, and Simulation

The advancements in systems and synthetic biology have been broadening the range of realizable systems with increasing complexity both in vitro and in vivo. Systems for digital logic operations, signal processing, analog computation, program flow control, as well as those composed of different functions – for example an on-site diagnostic system based on multiple biomarker measurements and signal processing – have been realized successfully. However, the efforts to date tend to tackle each design problem separately, relying on ad hoc strategies rather than providing more general solutions based on a unified and extensible architecture, resulting in long development cycle and rigid systems that require redesign even for small specification changes.Inspired by well-tested techniques adopted in electronics design automation (EDA), this work aims to remedy current design methodology by establishing a standardized, complete flow for realizing biomolecular systems. Given a behavior specification, the flow streamlines all the steps from modeling, synthesis, simulation, to final technology mapping onto implementing chassis. The resulted biomolecular systems of our design flow are all built on top of an FPGA-like reconfigurable architecture with recurring modules. Each module is designed the function of eachmodule depends on the concentrations of assigned auxiliary species acting as the “tuning knobs.” Reconfigurability not only simplifies redesign for altered specification or post-simulation correction, but also makes post-manufacture fine-tuning – even after system deployment – possible. This flexibility is especially important in synthetic biology due to the unavoidable variations in both the deployed biological environment and the biomolecular reactions forming the designed system.In fact, by combining the system’s reconfigurability and neural network’s self-adaptiveness through learning, we further demonstrate the high compatibility of neuromorphic computation to our proposed architecture. Simulation results verified that with each module implementing a neuron of selected model (ex. spike-based, threshold-gate-like, etc.), accompanied by an appropriate choice of reconfigurable properties (ex. threshold value, synaptic weight, etc.), the system built from our proposed flow can indeed perform desired neuromorphic functions

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