論文

Modeling and simulation of a cellular system on computers are now becoming an essential process in biological researches. However, modern PCs can't provide enough performance to simulate large-scale biochemical networks. ReCSiP is the alternative FPGA-based solution for biochemical simulations. In this paper, the novel method of biochemical simulation with multiple reaction models on an FPGA is proposed. The method generates optimal circuit and its optimal schedule for each simulation models written in SBML, the standard markup language in systems biology. ReCSiP has a Xilinx's XC2VP70 and achieved over 20-fold speedup compared to Intel's PentiumIII 1.13GHz.

In some applications on Dynamically Reconfigurable Processor (DRP), the input/output of data stream occupies about 20% of total execution time. In order to hide the overhead of input/output, the separation of the I/O context and double-buffering mechanism are proposed. Using the mechanism, I/O overhead of six streaming processing programs can be completely hidden. Based on the analysis of I/O performance, two alternatives for executing parallel processing with multiple DRP Cores are compared and discussed. © 2005 IEEE.

Today, mathematical modeling and simulation of biochemical pathways take a major role in biological researches. However, modern microprocessors cannot provide enough throughput to explore the large parameter space of target pathways. To address this problem, ReCSiP (a ReConfigurable Cell Simulation Platform), an FPGA-based biochemical simulator is proposed. It's an ODE-based simulator, which solves the rate-law functions. The framework proposed in this paper, enables to simulate pathways consisting many different types of chemical reactions by connecting the rate-law modules (solvers) on an FPGA. It provides the solver-to-solver communication mechanism on an FPGA and automatic configuration software to generate the circuit. © 2005 IEEE.

Resource reduction design techniques play an important role to implement large-scale FPGA-based accelerator systems in floating point applications since available resources on FPGAs are limited. This paper proposes a dataflow graph classification method which makes groups of graphs based on their similarity in order to bring out efficient graph combining. Aiming at finding effective parameters for the k-means algorithm, various parameter combinations are evaluated and compared in terms of resource reduction effects and performance. The experimental results using an FPGA-based biochemical simulator reveal that the graph clustering that uses information on the maximum common subgraphs achieve 73.3% of resource reduction rate while alleviating the performance degradation. © 2010 IEEE.

In order to simulate large scale biological models with a reconfigurable FPGA-based biochemical simulator system, reduction of required resources are essential. This paper proposes a method which combines common terms in rate law functions appeared in biochemical models and generates a shared hardware module used for numerical integration. In this approach, two functions are combined in a tree structure level, followed by pipeline scheduling and arithmetic module binding. The evaluation result reveals that this approach reduces hardware resources by 31.4 % on average at the cost of 14.4 % throughput degradation.

Recursive Diagonal Torus (RDT) is a class of interconnection network consisting of recursively overlaid two-dimensional square diagonal tori for massively parallel computers with up to 2(16) nodes. Connection structures of the RDT vary according to the assignment of tipper rank diagonal tori into a node: Although traditional simple assignment called RDT(2,4,1)/alpha shows enough performance under the uniform traffic, the congestion of low rank tori degrades the performance when local communication is dominant. In this paper RDT(2,4,1)/beta torus assignment is proposed, focusing an improving the performance for local communication. With a simplified simulation algorithm, result shows that RDT(2,4,1)/beta improves the average distance compared with RDT(2,4,1)/alpha assignment when considering local area.

近年, Personal computerを高速なネットワークで結び, 並列処理を行う研究がさかんに行われている.通常ネットワークはwiring flexibility, scalabilityの要求からトポロジに制限のないirregular networkが用いられるが, 既存のirregular networkのルーティングアルゴリズムは, チャネル使用制限が厳しく, リンクを有効に使うことができない.そこで本稿では各リンクを効果的に使うことを念頭においたadaptive routingであるLeft-up-first turn routing(L-turn routing)を提案する.L-turn routingはirregular networkをdirected-graphに見立てる際に工夫を施すことにより, デッドロックを防ぐために課すパケットの転送禁止ターンの分散を実現する点が特徴であり, 高いトラフィック分散能力を持つ.シミュレーション結果より, L-turn routingは様々なトポロジにおいて高い性能を示すことが分かった.

FPGAベースの生化学シミュレータでは,デバイスの資源制約を考慮した高スループットな回路設計が重要である.本稿では,データの入力ポートの制約とハードウェアコストの削減を考慮した演算パイプラインスケジューリング手法を提案する.入力データの優先付けを工夫して,優先順位にしたがって投入タイミングを決定することで,スケジューリングへの影響を小さくする方法を提案する.評価の結果,ALAPスケジューリングと比較して平均7.6%のハードウェア量を削減することができた.

The intracellular environment is crowded with skeletal proteins, organelle membranes, ribosomes, and so on. When molecular movement is restricted by such environments, some biochemical reaction processes cannot be represented by classical models, which assume that reactions occur in simple Newtonian fluids. Dimension-restricted reaction kinetics (DRRK) modeling is a method that can represent dimension-restricted reactions. We introduce the methods of DRRK in each case of reaction type. DRRK has another advantage in that it can be quantitatively evaluated by biochemical experiments. We also introduce the procedure of applying it for experimental results. This modeling method may provide the basis for in vivo-oriented modeling. © 2007 Humana Press Inc.