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In general, a detailed modeling and evaluation of computer architectures make a cycleaccurate simulator necessary. As the architectures become increasingly complex for parallel, cloud, and neural computing, nowadays, the complexity of the simulator grows rapidly, and thus its execution is too slow or infeasible for practical use. In order to alleviate the problem, many previous studies have focused on reducing the simulation time in a variety of ways such as using sampling methods, adding hardware accelerators, and so on. In this paper, we propose a new parallel simulation framework, called Epoch-based Parallel SIMulator, to obtain scalable speedup with large number of cores. The framework is based on a well-known cycle-accurate full-system simulator, MARSSx86. From the simulator, we build an epoch, that is an execution interval, where the architectural simulation by PTLSim does not involve any interaction with QEMU. Therefore, we can simulate epochs independently, i.e., execute multiple epochs completely in parallel by PTLSim with their live-in data. Our performance evaluation shows that we achieve 12.8× speed on average with 16-core parallel simulation from the SPEC CPU2006 benchmarks and the PARSEC benchmarks, providing the performance scalability.

We present a novel architecture of parallel contingency analysis that accelerates massive power flow computation using cloud computing. It leverages cloud computing to investigate huge power systems of various and potential contingencies. Contingency analysis is undertaken to assess the impact of failure of power system components; thus, extensive contingency analysis is required to ensure that power systems operate safely and reliably. Since many calculations are required to analyze possible contingencies under various conditions, the computation time of contingency analysis increases tremendously if either the power system is large or cascading outage analysis is needed. We also introduce a task management optimization to minimize load imbalances between computing resources while reducing communication and synchronization overheads. Our experiment shows that the proposed architecture exhibits a performance improvement of up to 35.32× on 256 cores in the contingency analysis of a real power system, i.e., KEPCO2015 (the Korean power system), by using a cloud computing system. According to our analysis of the task execution behaviors, we confirmed that the performance can be enhanced further by employing additional computing resources.

A power flow study aims to analyze a power system by obtaining the voltage and phase angle of buses inside the power system. Power flow computation basically uses a numerical method to solve a nonlinear system, which takes a certain amount of time because it may take many iterations to find the final solution. In addition, as the size and complexity of power systems increase, further computational power is required for power system study. Therefore, there have been many attempts to conduct power flow computation with large amounts of data using parallel computing to reduce the computation time. Furthermore, with recent system developments, attempts have been made to increase the speed of parallel computing using graphics processing units (GPU). In this review paper, we summarize issues related to parallel processing in power flow studies and analyze research into the performance of fast power flow computations using parallel computing methods with GPU.

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We synthesized 11 novel L-783277 derivatives, in which a structure rigidifying phenyl ring is incorporated into the 14-membered chiral resorcylic acid lactone system. The SAR study with these substances demonstrated that 17 possesses excellent kinase selectivity against a panel of 335 kinases in contrast to L-783277 and inhibits VEGFR3, VEGFR2, and FLT3 with single-digit nanomolar IC50 values. Also, we found that 21, a stereoisomer of 17, has excellent potency (IC50 = 9 nM) against VEGFR3 and selectivity over VEGFR2 and FLT3. 17, a potent dual VEGFR3 and VEGFR2 inhibitor, effectively suppresses both lymphangiogenesis and angiogenesis in a 3D-microfluidic tumor lymphangiogenesis assay and in vivo corneal assay while SAR131675 blocks only lymphangiogenesis. In addition, 17 blocks the endothelial tube formation and suppresses proliferation of PHE tumor vascular model. 17 will be a valuable templatefor developing therapeutically active and selective substances that target both lymphangiogenesis and angiogenesis.

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