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distributed computation: the new wave of synthetic biology devices

TRENDS IN BIOTECHNOLOGY 2012年第6期30卷 342-349页

作者： Macia, Javier Posas, Francesc Sole, Ricard V. Univ Pompeu Fabra ICREA Complex Syst Lab Dept Ciencies Expt & Salut Barcelona 08003 Spain CSIC UPF Inst Biol Evolut Barcelona 08003 Spain Univ Pompeu Fabra Cell Signaling Unit Dept Ciencies Expt & Salut Barcelona 08003 Spain Santa Fe Inst Santa Fe NM 87501 USA

Synthetic biology (SB) offers a unique opportunity for designing complex molecular circuits able to perform predefined functions. But the goal of achieving a flexible toolbox of reusable molecular components has been shown to be limited due to circuit unpredictability, incompatible parts or random fluctuations. Many of these problems arise from the challenges posed by engineering the molecular circuitry: multiple wires are usually difficult to implement reliably within one cell and the resulting systems cannot be reused in other modules. These problems are solved by means of a nonstandard approach to single cell devices, using cell consortia and allowing the output signal to be distributed among different cell types, which can be combined in multiple, reusable and scalable ways.

关键词： synthetic biology distributed computation molecular circuit

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Multi-Layer Decomposition of Network Utility Maximization Problems

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IEEE-ACM TRANSACTIONS ON NETWORKING 2020年第5期28卷 2077-2091页

作者： Karakoc, Nurullah Scaglione, Anna Nedic, Angelia Reisslein, Martin Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA

We describe a distributed framework for resource sharing problems that arise in communications, micro-economics, and various networking applications. In particular, we consider a hierarchical multi-layer decomposition for network utility maximization (ML-NUM), where functionalities are assigned to different layers. The proposed methodology creates solutions with central management and distributed computations to the resource allocation problems. In non-stationary environments, the technique aims to respond quickly to the dynamics of the network by decreasing delay by partially shifting the communication and computational burden to the network edges. Our main contribution is a detailed analysis under the assumption that the network changes are on the same time-scale as the convergence time of the algorithms used for local computations. Moreover, assuming strong concavity and smoothness of the users' objective functions, and under some stability conditions for each layer, we present convergence rates and optimality bounds for the ML-NUM framework. In addition, the main benefits of the proposed method are demonstrated with numerical examples.

关键词： Resource management Convergence Heuristic algorithms Indexes Gradient methods distributed algorithms IEEE transactions distributed computation network resource allocation

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Deep reinforcement learning based computation offloading and resource allocation for low-latency fog radio access networks

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Intelligent and Converged Networks 2020年第3期1卷 243-257页

作者： G.M.Shafiqur Rahman Tian Dang Manzoor Ahmed Key Laboratory of Universal Wireless Communications(Ministry of Education) Beijing University of Posts and TelecommunicationsBeijing 100876China

Fog Radio Access Networks(F-RANs)have been considered a groundbreaking technique to support the services of Internet of Things by leveraging edge caching and edge ***,the current contributions in computation offloading and resource allocation are inefficient;moreover,they merely consider the static communication mode,and the increasing demand for low latency services and high throughput poses tremendous challenges in F-RANs.A joint problem of mode selection,resource allocation,and power allocation is formulated to minimize latency under various *** propose a Deep Reinforcement Learning(DRL)based joint computation offloading and resource allocation scheme that achieves a suboptimal solution in *** core idea of the proposal is that the DRL controller intelligently decides whether to process the generated computation task locally at the device level or offload the task to a fog access point or cloud server and allocates an optimal amount of computation and power resources on the basis of the serving *** results show that the proposed approach significantly minimizes latency and increases throughput in the system.

关键词： fog radio access networks computation offloading mode selection resource allocation distributed computation low latency deep reinforcement learning

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A Strategy of Parallel Seed-Based Image Segmentation Algorithms for Handling Massive Image Tiles over the Spark Platform

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REMOTE SENSING 2021年第10期13卷 1969-1969页

作者： Chen, Fang Wang, Ning Yu, Bo Qin, Yuchu Wang, Lei Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci 9 Dengzhuang South Rd Beijing 100094 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Hainan Key Lab Earth Observat Sanya 572029 Peoples R China

The volume of remote sensing images continues to grow as image sources become more diversified and with increasing spatial and spectral resolution. The handling of such large-volume datasets, which exceed available CPU memory, in a timely and efficient manner is becoming a challenge for single machines. The distributed cluster provides an effective solution with strong calculation power. There has been an increasing number of big data technologies that have been adopted to deal with large images using mature parallel technology. However, since most commercial big data platforms are not specifically developed for the remote sensing field, two main issues exist in processing large images with big data platforms using a distributed cluster. On the one hand, the quantities and categories of official algorithms used to process remote sensing images in big data platforms are limited compared to large amounts of sequential algorithms. On the other hand, the sequential algorithms employed directly to process large images in parallel over a distributed cluster may lead to incomplete objects in the tile edges and the generation of large communication volumes at the shuffle stage. It is, therefore, necessary to explore the distributed strategy and adapt the sequential algorithms over the distributed cluster. In this research, we employed two seed-based image segmentation algorithms to construct a distributed strategy based on the Spark platform. The proposed strategy focuses on modifying the incomplete objects by processing border areas and reducing the communication volume to a reasonable size by limiting the auxiliary bands and the buffer size to a small range during the shuffle stage. We calculated the F-measure and execution time to evaluate the accuracy and execution efficiency. The statistical data reveal that both segmentation algorithms maintained high accuracy, as achieved in the reference image segmented in the sequential way. Moreover, generally the strategy took le

关键词： segmentation algorithm distributed computation image processing spark platform digital disaster reduction

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Self-adjusting Task Granularity for Global Load Balancer Library on Clusters of Many-core Processors 11

Self-adjusting Task Granularity for Global Load Balancer Lib...

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11th International Workshop on Programming Models and Applications for Multicores and Manycores (PMAM)

作者： Finnerty, Patrick Kamada, Tomio Ohta, Chikara Kobe Univ Grad Sch Syst Informat Kobe Hyogo Japan Kobe Univ Japan Grad Sch Sci Technol & Innovat Kobe Hyogo Japan

ISBN: (纸本)9781450375221

Achieving load balance is a challenge for irregular applications. Balancing runtimes and libraries aim at relieving the programmer from this difficult task by proposing a layer of abstraction between the computation at hand and the hardware used to actually perform it. With the rise of many-core architectures, load balancers for distributed computation are now expected to handle unbalance both between and within compute nodes. We bear a particular interest in backtrack search algorithms where branches of the exploration tree can be processed in parallel. Profile-based load balancers cannot be applied to them as the computation need only be performed once. It is therefore vital to promptly detect and address load unbalances from the start. A popular solution consists in using the fork/join model. Another approach on which we focus in this article is implemented in the load balancing library of X10. Both of these schemes are sensitive to task granularity. Fined-grained tasks may incur significant overhead, especially with the increased number of parallel workers of many-core architectures, while coarse-grained tasks may cause starvation. Determining a suitable grain size for a particular application is difficult. In this article we present our Java implementation of the X10 load balancing scheme. We extend it with a tuning mechanism which dynamically adjusts the task granularity, increasing the library's versatility and usability. We demonstrate the capabilities of our tuning mechanism to suit many-core clusters using four applications: Unbalanced Tree Search, Pentomino, the Traveling Salesman Problem, and N-Queens. We achieve near identical performance to the ideal fixed grain executions on the first three but suffer from a more significant performance gap on the N-Queens problem. We advance hypotheses as to why.

关键词： task-granularity auto-tuning many-core clusters load balancing distributed computation

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Field-programmable biological circuits and configurable (bio)logic blocks for distributed biological computing

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COMPUTERS IN BIOLOGY AND MEDICINE 2021年 128卷 104109-104109页

作者： Moskon, Miha Pusnik, Ziga Zimic, Nikolaj Mraz, Miha Univ Ljubljana Fac Comp & Informat Sci Ljubljana Slovenia

Synthetic biology applications often require engineered computing structures, which can be programmed to process the information in a given way. However, programming of these structures usually requires significant amount of trial-and-error genetic engineering. This process is to some degree analogous to the design of application-specific integrated circuits (ASIC) in the domain of digital electronic circuits, which often require complex and time-consuming workflows to obtain a desired response. We describe a design of programmable biological circuits that can be configured without additional genetic engineering. Their configuration can be changed in vivo, i.e. during the execution of their biological program, simply with an introduction of programming inputs. These, e.g., increase the degradation rates of selected proteins that store the current configuration of the circuit. Programming can be thus performed in the field as in the case of field-programmable gate array (FPGA) circuits, which present an attractive alternative of ASICs in digital electronics. We describe a basic programmable unit, which we denote configurable (bio)logical block (CBLB) inspired by the architecture of configurable logic blocks (CLBs), basic functional units within the FPGA circuits. The design of a CBLB is based on distributed cellular computing modules, which makes its biological implementation easier to achieve. We establish a computational model of a CBLB and analyse its response with a given set of biologically feasible parameter values. Furthermore, we show that the proposed CBLB design exhibits correct behaviour for a vast range of kinetic parameter values, different population ratios, and as well preserves this response in stochastic simulations.

关键词： Field-programmable biological circuits Configurable (bio)logic blocks distributed computation Biological wires Multiplexer Memory

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Bivariate Hermitian Polynomial Coding for Efficient distributed Matrix Multiplication

Bivariate Hermitian Polynomial Coding for Efficient Distribu...

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IEEE Global Communications Conference (GLOBECOM) on Advanced Technology for 5G Plus

作者： Hasircioglu, Burak Gomez-Vilardebo, Jesus Gunduz, Deniz Imperial Coll London London England Ctr Tecnol Telecomunicac Catalunya CTTC CERCA Barcelona Spain

ISBN: (纸本)9781728182988

Coded distributed computing is an effective framework to improve the speed of distributed computing systems by mitigating stragglers (temporarily slow workers). In essence, coded computing allows replacing the computation assigned to a straggling worker by that at a faster worker by assigning redundant computations. Coded computing techniques proposed so far are mostly based on univariate polynomial coding. These codes are not very effective if storage and computation capacity across workers are heterogeneous and lose completely the work done by the straggling workers. For the particular problem of distributed matrix-matrix multiplication, we show how bivariate polynomial coding addresses these two issues.

关键词： coded computation distributed computation distributed matrix multiplication bivariate polynomial coding

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Marabunta: Continuous distributed Processing of Skewed Streams 20

Marabunta: Continuous Distributed Processing of Skewed Strea...

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20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid)

作者： Li, Bing Zhang, Zhibin Zheng, Tianqi Zhong, Qiaoling Huang, Qun Cheng, Xueqi Chinese Acad Sci Inst Comp Technol CAS Key Lab Network Data Sci & Technol Beijing Peoples R China Univ Chinese Acad Sci Sch Comp & Control Engn Beijing Peoples R China Chinese Acad Sci Inst Comp Technol State Key Lab Comp Architecture Beijing Peoples R China

ISBN: (数字)9781728160955

ISBN: (纸本)9781728160955

Current stream processing systems (SPSs) suffer from the imbalanced load and limited parallelism due to skewed data distributions and imbalanced computational resources. We observed that the cause of these problems is current SPSs partition their workloads statically. To address this problem, we design a distributed stream processing system, Marabunta, for skewed stream processing. Marabunta performs dynamic scaling and load balancing automatically at runtime. Large partitions in a skewed data distribution can be processed in parallel or migrated to idle machines to achieve load balancing. Moreover, Marabunta uses a new execution model to accelerate the execution by increases the parallelism and the computational resources utilization. We implemented Marabunta in C++ and optimized it for modern hardware. Our evaluations on typical streaming workloads show that Marabunta achieves higher throughputs and better elasticity with both uniform and skewed datasets compared to the state-of-the-art SPSs, e.g., Flink and Heron.

关键词： distributed computation data stream processing skewed data processing dynamic scheduling

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Coded Sequential Matrix Multiplication for Straggler Mitigation

IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY

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IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY 2021年第3期2卷 830-844页

作者： Krishnan, M. Nikhil Hosseini, Erfan Khisti, Ashish Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada Int Inst Informat Technol Bangalore Bengaluru 560100 India

In this work, we consider a sequence of J matrix multiplication jobs which needs to be distributed by a master across multiple worker nodes. For i is an element of{1, 2,..., J}, job-i begins in round-i and has to be completed by round-(i + T). In order to provide resiliency against slow workers (stragglers), previous works focus on coding across workers, which is the special case of T = 0. We propose here two schemes with T > 0, which allow for coding across workers as well as the dimension of time. Our first scheme is a modification of the polynomial coding scheme introduced by Yu et al. and places no assumptions on the straggler model. Exploitation of the temporal dimension helps the scheme handle a larger set of straggler patterns than the polynomial coding scheme, for a given computational load per worker per round. The second scheme assumes a particular straggler model to further improve performance (in terms of encoding/decoding complexity). We develop theoretical results establishing (i) optimality of our proposed schemes for certain classes of straggler patterns and (ii) improved performance for the case of i.i.d. stragglers. These are further validated by experiments, where we implement our schemes to train neural networks.

关键词： distributed computation matrix multiplication polynomial codes erasure coding

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Bivariate Polynomial Coding for Efficient distributed Matrix Multiplication

IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY

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IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY 2021年第3期2卷 814-829页

作者： Hasircioglu, Burak Gomez-Vilardebo, Jesus Gunduz, Deniz Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England Ctr Tecnol Telecomun Catalunya CTTC CERCA Barcelona 08860 Spain

Coded computing is an effective technique to mitigate "stragglers" in large-scale and distributed matrix multiplication. In particular, univariate polynomial codes have been shown to be effective in straggler mitigation by making the computation time depend only on the fastest workers. However, these schemes completely ignore the work done by the straggling workers resulting in a waste of computational resources. To reduce the amount of work left unfinished at workers, one can further decompose the matrix multiplication task into smaller sub-tasks, and assign multiple sub-tasks to each worker, possibly heterogeneously, to better fit their particular storage and computation capacities. In this work, we propose a novel family of bivariate polynomial codes to efficiently exploit the work carried out by straggling workers. We show that bivariate polynomial codes bring significant advantages in terms of upload communication costs and storage efficiency, measured in terms of the number of sub-tasks that can be computed per worker. We propose two bivariate polynomial coding schemes. The first one exploits the fact that bivariate interpolation is always possible on a rectangular grid of evaluation points. We obtain such points at the cost of adding some redundant computations. For the second scheme, we relax the decoding constraints and require decodability for almost all choices of the evaluation points. We present interpolation sets satisfying such decodability conditions for certain storage configurations of workers. Our numerical results show that bivariate polynomial coding considerably reduces the average computation time of distributed matrix multiplication. We believe this work opens up a new class of previously unexplored coding schemes for efficient coded distributed computation.

关键词： Bivariate polynomial interpolation coded computation distributed computation distributed matrix multiplication polynomial codes

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