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This paper proposes a novel FPGA-based matrix-inversion technique that is specifically tailored and optimized for real-time electromagnetic transients simulation of power electronic converters with high switching frequency. This is the first reported solution that is capable of solving the real-time equations related to using ideal switch model and the associated circuitry in very small time-steps (e.g., an average of 36 ns in a three-phase back-to-back converter case study), without requiring large amount of memory, being limited to small number of switches, adding parasitic elements, or depending on a priori knowledge of the circuit operation or switching strategy. The accuracy of the proposed real-time simulator is verified against the results from experimental measurements. [ABSTRACT FROM AUTHOR]

One of the key objectives in many fields in machine learning is to discover causal relationships among a set of variables from observational data. In linear non-Gaussian acyclic models (LiNGAM), it can be shown that the true underlying causal structure can be identified uniquely from merely observational data. The DirectLiNGAM algorithm is a well-known solution to learn the true causal structure in a high dimensional setting. DirectLiNGAM algorithm executes in a sequence of iterations and it performs a set of comparisons between pairs of variables in each iteration. Unfortunately, the runtime of this algorithm grows significantly as the number of variables increases. In this paper, we propose a parallel algorithm, called ParaLiNGAM, to learn casual structures based on DirectLiNGAM algorithm. We propose a threshold mechanism that can reduce the number of comparisons remarkably compared with the sequential solution. Moreover, in order to further reduce runtime, we employ a messaging mechanism between workers. We also present an implementation of ParaLiNGAM on GPU, considering hardware constraints. Experimental results on synthetic and real data show that our proposed solution outperforms DirectLiNGAM by a factor up to 4788X, and by a median of 2344X. • A scalable algorithm for learning causal structures in linear non-Gaussian models. • Significant speedup by selecting the necessary computations judiciously. • Speedup ratio of up to 4788 on GPUs in real-world and synthetic datasets. [ABSTRACT FROM AUTHOR]

We study the problem of mapping concurrent tasks of an application to cores of a chip multiprocessor that utilize circuit-switched interconnect and global asynchronous local synchronous (GALS) clocking domains. We develop a configurable algorithm that naturally handles a number of practical requirements, such as architectural features of the target platform, core failures, and hardware accelerators, and in addition, is scalable to a large number of tasks and cores. Experiments with several real life applications show that our algorithm outperforms manual mapping, integer linear programming-based mapping after ten days of solver run time, and a recent packet-switched network on chip-based task mapper through which, we underscore the unique requirements of task mapping for circuit-switched GALS architectures. [ABSTRACT FROM PUBLISHER]

We study the problem of experiment design to learn causal structures from interventional data. We consider an active learning setting in which the experimenter decides to intervene on one of the variables in the system in each step and uses the results of the intervention to recover further causal relationships among the variables. The goal is to fully identify the causal structures with minimum number of interventions. We present the first deep reinforcement learning based solution for the problem of experiment design. In the proposed method, we embed input graphs to vectors using a graph neural network and feed them to another neural network which outputs a variable for performing intervention in each step. Both networks are trained jointly via a Q-iteration algorithm. Experimental results show that the proposed method achieves competitive performance in recovering causal structures with respect to previous works, while significantly reducing execution time in dense graphs. • Active learning of causal structures by training an agent with a deep reinforcement learning algorithm. • The agent is trained by jointly optimizing an embedding network and a score network. • The proposed solution achieves competitive performance compared with previous work while reducing execution time. [ABSTRACT FROM AUTHOR]

Heterogeneous soft multiprocessor systems are likely to find a larger share in the application-specific computing market due to increasing cost and defect rates in foreseeable manufacturing technologies. We study the problem of mapping streaming applications onto heterogeneous soft dual-processor systems, in which processors' limited memory resources and application throughput form the outstanding constraints and objective, respectively. A key step in the compilation process is task assignment, where tasks are assigned to the processors. We develop a provably-effective algorithm for task assignment. Our algorithm is versatile, in that its formal properties hold for, and hence it is applicable to, a variety of platforms. Measurement of generated code size, and throughput of emulated systems validate the effectiveness of our approach. We advance the state-of-the-art by considerably outperforming two recent competitors in terms of both versatility and application throughput. [ABSTRACT FROM AUTHOR]

Given a social network modeled as a weighted graph $G$ G , the influence maximization problem seeks $k$ k vertices to become initially influenced, to maximize the expected number of influenced nodes under a particular diffusion model. The influence maximization problem has been proven to be NP-hard, and most proposed solutions to the problem are approximate greedy algorithms, which can guarantee a tunable approximation ratio for their results with respect to the optimal solution. The state-of-the-art algorithms are based on Reverse Influence Sampling (RIS) technique, which can offer both computational efficiency and non-trivial $(1-{1}/{e}-\epsilon)$ (1 - 1 / e - ε) -approximation ratio guarantee for any $\epsilon >0$ ε > 0 . RIS-based algorithms, despite their lower computational cost compared to other methods, still require long running times to solve the problem in large-scale graphs with low values of $\epsilon$ ε . In this article, we present a novel and efficient parallel implementation of a RIS-based algorithm, namely IMM, on GPU. The proposed GPU-accelerated influence maximization algorithm, named gIM, can significantly reduce the running time on large-scale graphs with low values of $\epsilon$ ε . Furthermore, we show that gIM algorithm can solve other variations of the IM problem, only by applying minor modifications. Experimental results show that the proposed solution reduces the runtime by a factor up to 220 ×. The source code of gIM is publicly available online. [ABSTRACT FROM AUTHOR]

The main goal in many fields in the empirical sciences is to discover causal relationships among a set of variables from observational data. PC algorithm is one of the promising solutions to learn underlying causal structure by performing a number of conditional independence tests. In this paper, we propose a novel GPU-based parallel algorithm, called cuPC, to execute an order-independent version of PC. The proposed solution has two variants, cuPC-E and cuPC-S, which parallelize PC in two different ways for multivariate normal distribution. Experimental results show the scalability of the proposed algorithms with respect to the number of variables, the number of samples, and different graph densities. For instance, in one of the most challenging datasets, the runtime is reduced from more than 11 hours to about 4 seconds. On average, cuPC-E and cuPC-S achieve 500X and 1300X speedup, respectively, compared to serial implementation on CPU. [ABSTRACT FROM AUTHOR]