Recent years have seen a surge in research on dynamic graph representation learning, which aims to model temporal graphs that are and evolving constantly over time. However, current work typically models dynamics with recurrent neural networks (RNNs), making them suffer seriously from computation memory overheads large graphs. So far, scalability of learning remains one the major challenges. In...

Multi-Supply voltage design using Cluster Voltage Scaling (CVS) is an effective way to reduce power consumption without performance degradation. One of the major issues in this method is performance and power overheads due to insertion of Level Converting Flip-Flops (LCFF) at the interface from low-supply to high-supply clusters to simultaneously perform latching and level conversion. In this p...

K-means is a popular clustering method used in data mining area. To work with large datasets, researchers propose PKMeans, which is a parallel k-means on MapReduce [3]. However, the existing k-means parallelization methods including PKMeans have many limitations. It can’t finish all its iterations in one MapReduce job, so it has to repeat cascading MapReduce jobs in a loop until convergence. On...

This paper introduces a novel theoretical framework for the analysis of the communication overheads associated with loop nest tiling on distributed-memory systems.

We propose MUMBO, the first high-performing yet computationally efficient acquisition function for multi-task Bayesian optimization. Here, challenge is to perform optimization by evaluating low-cost functions somehow related our true target function. This a broad class of problems including popular task multi-fidelity However, while information-theoretic are known provide state-of-the-art optim...

Wireless internet services are quickly intensifying and improving, it is significant to provide users with not only high speed and high quality wireless service but also secured. Multihop relay-based support was added, which not only help for improving coverage and throughput but also provides features such as lower backhaul deployment cost, easy setup, robustness and re-configurability, which ...

In this paper, we address the problem of transparently scaling out transactional (OLTP) workloads on relational databases, to support database-as-a-service in cloud computing environment. The primary challenges in supporting such workloads include choosing how to partition the data across a large number of machines, minimizing the number of distributed transactions, providing high data availabi...

Parallel iterative solvers are often the only means of solving large linear systems and eigenproblems. However, these solvers are usually implemented in a fine grain manner and, when scaled to large numbers of processors on MPP’s, can incur significant performance penalties due to synchronization overheads. This problem is exacerbated in clusters of workstations (COWs) and SMPs that are interco...

The development of emerging applications, such as autonomous transportation systems, is expected to result in an explosive growth mobile data traffic. As the available spectrum resource becomes more and scarce, there a growing need for paradigm shift from Shannon's Classical Information Theory (CIT) semantic communication (SemCom). Specifically, former adopts “transmit-before-understanding” app...