Parallel Transient Stability-Constrained Optimal Power Flow Using GPU as Coprocessor

Transient stability constrained optimal power flow (TSCOPF) is one the most computational-intensive applications in power systems. Research efforts were made to utilize high performance computing (HPC) technology to parallelize and accelerate TSCOPF solving process, but challenges still exist in performance analysis to identify bottlenecks and improve efficiency on practical HPC platforms such as Beowulf clusters. Based on first two authors' previous work [8, 12] - two-level parallel TSCOPF with reduced-space interior point method (RIPM), a systematic performance analysis approach is demonstrated in this paper. Comprehensive performance analysis procedures - wall time analysis, MPI/OpenMP profiling and tracing - is employed in order to discover performance bottlenecks. State-of-the-art performance analysis software are employed to generate and visualize performance data, providing guidelines for parallel performance enhancement. Numerical results on a 2746-bus system show effectiveness of the proposed approach and relative low overhead in solving large-scale multi-contingency TSCOPF on a Beowulf cluster.

A novel approach based on both single machine equivalent (SIME) and trajectory sensitivity methods is proposed to formulate a transient stability-constrained optimal power flow (TSC-OPF) in the Euclidian space, where only one single stability constraint is necessary in the optimization problem to represent all dynamic and transient stability constraints of the multi-machine system, resulting in a tractable approach to the preventive control of transient stability in realistic power systems. A unified framework of time domain analysis is proposed, where the transient stability, trajectory sensitivity and SIME analyses are all combined to assess the system's stability and to compute the sensitivity coefficients of the proposed transient stability constraint. Based on these sensitivity coefficients, a non-heuristic selection criterion is proposed to perform the preventive control by rescheduling only a selected number of generators, which is the commonly accepted practice followed by the system's operators. The validity and the effectiveness of the proposed method is numerically demonstrated in the Mexican 46-machine, 190-bus system.

This paper addresses the fundamental issue regarding how to perform the preventive control of transient dynamics associated with rotor angles and nodal voltage magnitudes to obtain a transiently stable operating point for a given contingency scenario. The problem is formulated as a single contingency transient stability-constrained optimal power flow (TSC-OPF) model, in which the optimization and the transient stability problems are sequentially solved until a suitable generation dispatch that provides a transiently stable equilibrium point is assessed. A relevant feature of the proposed approach is its formulation in the form of a conventional OPF problem, which includes two power re-dispatch constraints that are only a function of the steady state active power output of generators. Hence, the control objective is achieved by constraining the direction and magnitude of the steady-state active power re-dispatch of the generators. The effectiveness of the proposed approach is demonstrated on the WSCC 3machine-9 bus test system.

Transient stability constrained optimal power flow considering fourth-order synchronous generator model and controls

Consideration of transient stability constraints in optimal power flow (OPF) problems is increasingly important because modern power systems tend to operate closer to stability boundaries due to the rapid increase of electricity demand and the deregulation of electricity markets. Transient stability constrained OPF (TSCOPF) is however a nonlinear optimization problem with both algebraic and differential equations, which is difficult to be solved even for small power systems. This paper develops a robust and efficient method for solving TSCOPF problems based on differential evolution (DE), which is a new branch of evolutionary algorithms with strong ability in searching global optimal solutions of highly nonlinear and nonconvex problems. Due to the flexible properties of DE mechanism, the hybrid method for transient stability assessment, which combines time-domain simulation and transient energy function method, can be employed in DE so that the detailed dynamic models of the system can be incorporated. To reduce the computational burden, several strategies are proposed for the initialization, assessment and selection of solution individuals in evolution process of DE. Numerical tests on the WSCC three-generator, nine-bus system and New England ten-generator, 39-bus system have demonstrated the robustness and effectiveness of the proposed approach. Finally, in order to deal with the large-scale system and speed up the computation, DE is parallelized and implemented on a Beowulf PC-cluster. The effectiveness of the parallel DE approach is demonstrated by simulations on the 17-generator, 162-bus system.

Graphics Processing Units (GPUs) have been widely used as accelerators in large-scale heterogeneous computing systems. However, current programming models can only support the utilization of local GPUs. When using non-local GPUs, programmers need to explicitly call API functions for data communication across computing nodes. As such, programming GPUs in large-scale computing systems is more challenging than local GPUs since local and remote GPUs have to be dealt with separately. In this work, we propose a virtual OpenCL (VOCL) framework to support the transparent virtualization of GPUs. This framework, based on the OpenCL programming model, exposes physical GPUs as decoupled virtual resources that can be transparently managed independent of the application execution. To reduce the virtualization overhead, we optimize the GPU memory accesses and kernel launches. We also extend the VOCL framework to support live task migration across physical GPUs to achieve load balance and/or quick system maintenance. Our experiment results indicate that VOCL can greatly simplify the task of programming cluster-based GPUs at a reasonable virtualization cost.

Large scale parallelisation of the material point method with multiple GPUs

The modern graphics processing unit (GPU) found in many standard personal computers is a highly parallel math processor capable of nearly 1 TFLOPS peak throughput at a cost similar to a high-end CPU and an excellent FLOPS/watt ratio. High-level linear algebra operations are computationally intense, often requiring O(N3) operations and would seem a natural fit for the processing power of the GPU. Our work is on CULA, a GPU accelerated implementation of linear algebra routines. We present results from factorizations such as LU decomposition, singular value decomposition and QR decomposition along with applications like system solution and least squares. The GPU execution model featured by NVIDIA GPUs based on CUDA demands very strong parallelism, requiring between hundreds and thousands of simultaneous operations to achieve high performance. Some constructs from linear algebra map extremely well to the GPU and others map poorly. CPUs, on the other hand, do well at smaller order parallelism and perform acceptably during low-parallelism code segments. Our work addresses this via hybrid a processing model, in which the CPU and GPU work simultaneously to produce results. In many cases, this is accomplished by allowing each platform to do the work it performs most naturally.

The ever increasing complexity of scientific applications has led to utilization of new HPC paradigms such as Graphical Processing Units (GPUs). However, modifying existing applications to enable them to be executed on GPU can be challenging. Furthermore, the considerable speedup achieved by execution of linear algebra operations on GPUs has added a huge heterogeneity to HPC clusters. In this work, we enabled NPAIRS, a neuro-imaging application, to be executed on GPU with slight modifications to its original code. This important feature of our implementation enables current users of NPAIRS, i.e. non-expert bio-medical scientists, to get benefit from GPU without having to apply fundamental changes to their existing application. As the second part of our research, we investigated the efficiency of several scheduling algorithms for a heterogeneous cluster that contains GPU nodes. Experimental results show that we achieved 7× speedup for NPAIRS. Moreover, although scheduling does not play an important role when there is no GPU node in the cluster, it can highly improve the makespan for a CPU-GPU cluster. We compared our scheduling results with Torque and MCT, two of the most commonly used schedulers in current HPC platforms. Our results show that the Sufferage scheduling can improve the makespan of Torque and MCT by 47% and 4% respectively.

It is known that parallel computing systems have some advantages to solve large scale and hard problems. However, traditional multi-processor parallel architectures are not widely used since they are hard to operate and they have high cost in setting-up .It has been an important point to Graphical Processing Units (GPU) were shown to be used in general-purpose applications because they naturally have hundreds of parallel cores. Studies related with the usage of GPUs in general-purpose are increasing day by day. In this study, Fast Fourier Transform (FFT), Inverse Fast Fourier Transform (IFFT) and Radon Transforms widely used in signal and image processing problems have been implemented on Quadro FX 3800 GPU which is one of the recent graphical cards of NVIDIA. From the results, applications on GPU provided gain in terms of time as compared to the applications implemented on traditional Central Processing Unit (CPU).

Summary form only given. Present day computers equipped with powerful graphics processing units (GPUs) show considerable promise of increased performance for the electromagnetic (EM) modeler. In order to determine the degree of performance gain achievable for electro-energetic physics computations, the MAGIC EM finite difference-time domain (FDTD) particle-in-cell (PIC) plasma code is undergoing testing for parallel speedup on typical large scale plasma wave EM calculations using GPU processing. The results of the tests are expected to be of great interest to the EM community because MAGIC is widely employed and implements the methods common to many large scale applications. The performance data obtained will quantify the benefits of GPUs, and aid in selecting the proper system including GPUs potentially critical to effectiveness with EM/plasma codes where calculation times often run into days due to small zone and time step requirements. GPUs promise a relatively low cost boost leveraging the vast development driven by the computer game industry.

Prediction of residual stresses in multi-pass welded joint using Idealized Explicit FEM accelerated by a GPU

Today, many high performance computing platforms use hybrid architectures combining multi-core processors and hardware accelerators like GPUs (Graphic Processing Units). This paper presents a new method for scheduling tasks for biological sequence comparison applications with CPUs and GPUs. This strategy is called SWDUAL and is based on a dual approximation scheme for determining which tasks are most suitable to be executed on the GPUs. The objective is to obtain fast execution time and minimize the idle time on each PE (Processing Element). It is implemented using a master-slave model. Results obtained when sequences were compared to five public genomic databases show that this method allows to reduce the execution time on hybrid platforms when compared to other public available implementations.

While the advances in synchrotron light sources, together with the development of focusing optics and detectors, allow nanoscale ptychographic imaging of materials and biological specimens, the corresponding experiments can yield terabyte-scale volumes of data that can impose a heavy burden on the computing platform. Although graphics processing units (GPUs) provide high performance for such large-scale ptychography datasets, a single GPU is typically insufficient for analysis and reconstruction. Several works have considered leveraging multiple GPUs to accelerate the ptychographic reconstruction. However, most of these works utilize only the Message Passing Interface to handle the communications between GPUs. This approach poses inefficiency for a hardware configuration that has multiple GPUs in a single node, especially while reconstructing a single large projection, since it provides no optimizations to handle the heterogeneous GPU interconnections containing both low-speed (e.g., PCIe) and high-speed links (e.g., NVLink). In this paper, we provide an optimized intranode multi-GPU implementation that can efficiently solve large-scale ptychographic reconstruction problems. We focus on the maximum likelihood reconstruction problem using a conjugate gradient (CG) method for the solution and propose a novel hybrid parallelization model to address the performance bottlenecks in the CG solver. Accordingly, we have developed a tool, called PtyGer (Ptychographic GPU(multiple)-based reconstruction), implementing our hybrid parallelization model design. A comprehensive evaluation verifies that PtyGer can fully preserve the original algorithm’s accuracy while achieving outstanding intranode GPU scalability.

The accelerating progress and availability of low cost computers, high speed networks, and software for high performance distributed computing allow us to reconsider computationally expensive techniques in image processing and pattern recognition. We propose a two-level hierarchical [Formula: see text]-nearest neighbor classifier where the first level uses graphics processor units (GPUs) and the second level uses a high performance cluster (HPC). The system is evaluated on the problem of character recognition with nine databases (Arabic digits, Indian digits (Bangla, Devnagari, and Oriya), Bangla characters, Indonesian characters, Arabic characters, Farsi characters and digits). Contrary to many approaches that tune the model for different scripts, the proposed image classification method is unchanged throughout the evaluation on the nine databases. We show that a hierarchical combination of decisions based on two distances, using GPUs and a HPC provides state-of-the-art performances on several scripts, and provides a better accuracy than more complex systems.