Biblio
Cooperation of software and hardware with hybrid architectures, such as Xilinx Zynq SoC combining ARM CPU and FPGA fabric, is a high-performance and low-power platform for accelerating RSA Algorithm. This paper adopts the none-subtraction Montgomery algorithm and the Chinese Remainder Theorem (CRT) to implement high-speed RSA processors, and deploys a 48-node cluster infrastructure based on Zynq SoC to achieve extremely high scalability and throughput of RSA computing. In this design, we use the ARM to implement node-to-node communication with the Message Passing Interface (MPI) while use the FPGA to handle complex calculation. Finally, the experimental results show that the overall performance is linear with the number of nodes. And the cluster achieves 6× 9× speedup against a multi-core desktop (Intel i7-3770) and comparable performance to a many-core server (288-core). In addition, we gain up to 2.5× energy efficiency compared to these two traditional platforms.
Tensor decompositions, which are factorizations of multi-dimensional arrays, are becoming increasingly important in large-scale data analytics. A popular tensor decomposition algorithm is Canonical Decomposition/Parallel Factorization using alternating least squares fitting (CP-ALS). Tensors that model real-world applications are often very large and sparse, driving the need for high performance implementations of decomposition algorithms, such as CP-ALS, that can take advantage of many types of compute resources. In this work we present ReFacTo, a heterogeneous distributed tensor decomposition implementation based on DeFacTo, an existing distributed memory approach to CP-ALS. DFacTo reduces the critical routine of CP-ALS to a series of sparse matrix-vector multiplications (SpMVs). ReFacTo leverages GPUs within a cluster via MPI to perform these SpMVs and uses OpenMP threads to parallelize other routines. We evaluate the performance of ReFacTo when using NVIDIA's GPU-based cuSPARSE library and compare it to an alternative implementation that uses Intel's CPU-based Math Kernel Library (MKL) for the SpMV. Furthermore, we provide a discussion of the performance challenges of heterogeneous distributed tensor decompositions based on the results we observed. We find that on up to 32 nodes, the SpMV of ReFacTo when using MKL is up to 6.8× faster than ReFacTo when using cuSPARSE.
In this paper, parallelization and high performance computing are utilized to enable ultrafast transient stability analysis that can be used in a real-time environment to quickly perform “what-if” simulations involving system dynamics phenomena. EPRI's Extended Transient Midterm Simulation Program (ETMSP) is modified and enhanced for this work. The contingency analysis is scaled for large-scale contingency analysis using Message Passing Interface (MPI) based parallelization. Simulations of thousands of contingencies on a high performance computing machine are performed, and results show that parallelization over contingencies with MPI provides good scalability and computational gains. Different ways to reduce the Input/Output (I/O) bottleneck are explored, and findings indicate that architecting a machine with a larger local disk and maintaining a local file system significantly improve the scaling results. Thread-parallelization of the sparse linear solve is explored also through use of the SuperLU_MT library.