A Multi-core High Performance Computing Framework for Probabilistic Solutions of Distribution Systems

<p>Multi-core CPUs with multiple levels of parallelism and deep memory hierarchies have become the mainstream computing platform. In this paper we developed a generally applicable high performance computing framework for Monte Carlo simulation (MCS) type applications in distribution systems, taking advantage of performance-enhancing features of multi-core CPUs. The application in this paper is to solve the probabilistic load flow (PLF) in real time, in order to cope with the uncertainties caused by the integration of renewable energy resources. By applying various performance optimizations and multi-level parallelization, the optimized MCS solver is able to achieve more than 50% of a CPU's theoretical peak performance and the performance is scalable with the hardware parallelism. We tested the MCS solver on the IEEE 37-bus test feeder using a new Intel Sandy Bridge multi-core CPU. The optimized MCS solver is able to solve millions of load flow cases within a second, enabling the real-time Monte Carlo solution of the PLF.</p

Barometric and GPS altitude sensor fusion

<p>The altitude of a moving vehicle as reported by GPS suffers from intermittent errors caused by temporary obstruction of the satellites by buildings, mountains, etc. Additionally, it is affected by systematic errors caused by multipath effects, ionospheric and tropospheric effects, and other hardware design limitations and natural factors. Atmospheric pressure, measured by a portable barometric sensor, could also be used to determine altitude, is not susceptible to problems caused by obstruction of satellites, and can provide reliable measurements outdoors even in urban and mountainous regions. In this paper, we propose an algorithm which improves accuracy and provides tighter confidence bounds of altitude measurements from a mobile phone (or any device equipped with GPS and barometric sensors) by means of sensor fusion techniques without the need for calibration. Our experiments have shown that the proposed algorithm provides more accurate measurements with tighter confidence bounds compared to using either of the two sensors, barometric or GPS, alone.</p

Power System Probabilistic and Security Analysis on Commodity High Performance Computing Systems

<p>Large scale integration of stochastic energy resources in power systems requires probabilistic analysis approaches for comprehensive system analysis. The large-varying grid condition on the aging and stressed power system infrastructures also requires merging of offline security analyses into online operation. Meanwhile in computing, the recent rapid hardware performance growth comes from the more and more complicated architecture. Fully utilizing the computing power for specific applications becomes very difficult. Given the challenges and opportunities in both the power system and the computing fields, this paper presents the unique commodity high performance computing system solutions to the following fundamental tools for power system probabilistic and security analysis: 1) a high performance Monte Carlo simulation (MCS) based distribution probabilistic load flow solver for real-time distribution feeder probabilistic solutions. 2) A high performance MCS based transmission probabilistic load flow solver for transmission grid probabilistic analysis. 3) A SIMD accelerated AC contingency calculation solver based on Woodbury matrix identity on multi-core CPUs. By aggressive algorithm level and computer architecture level performance optimizations including optimized data structures, optimization for superscalar out-of-order execution, SIMDization, and multi-core scheduling, our software fully utilizes the modern commodity computing systems, makes the critical and computational intensive power system probabilistic and security analysis problems solvable in real-time on commodity computing systems.</p

A Multi-core High Performance Computing Framework for Distribution Power Flow

There is an enormous growth in performance capability of computing platform in the last decade. The parallelism becomes an inevitable trend for future computing hardware / software design. Motivated by the practical computation performance demands in power system, especially distribution system, and the advances in modern computing platform, we developed a high performance parallel distribution power flow solver for Monte Carlo styled application. From computer architecture and programming point of view, we show that by applying various performance tuning techniques and parallelization, our distribution power flow solver is able to achieve 50% of a CPU's theoretical peak performance. That is 50x speedup comparing to an already fully compiler-optimized C++ implementation.</p

A Quasi-Monte Carlo Approach for Radial Distribution System Probabilistic Load Flow

<p>Monte Carlo simulation (MCS) is a numerical method to solve the probabilistic load flow (PLF) problem. Comparing to analytical methods, MCS for PLF has advantages such as flexibility, general purpose, able to deal with large nonlinearity and large variances, and embarrassingly parallelizable. However, MCS also suffers from low convergence speed and high computational burden, especially for problems with multiple random variables. In this paper, we proposed a Quasi-Monte Carlo (QMC) based method to solve the PLF for radial distribution network. QMC uses samples from low-discrepancy sequence intended to cover the high dimension random sample space as uniformly as possible. The QMC method is particularly suitable for the high dimension problems with low effective dimensions, and has been successfully used to solve large scale problems in econometrics and statistical circuit design. In this paper, we showed that the PLF for radial distribution system has the similar properties and can be a good candidate for QMC method. The proposed method possesses the advantage of MCS method and significantly increases the convergence rate and overall speed. Numerical experiment results on IEEE test feeders have shown the effectiveness of the proposed method.</p

Smart Memory Synthesis for Energy-Efficient Computed Tomography Reconstruction

<p>As nanoscale lithography challenges mandate greater pattern regularity and commonality for logic and memory circuits, new opportunities are created to affordably synthesize more powerful smart memory blocks for specific applications. Leveraging the ability to embed logic inside the memory block boundary, this paper demonstrates the synthesis of smart memory architectures that exploits the inherent memory address patterns of the back projection algorithm to enable efficient image reconstruction at minimum hardware overhead. An end-to-end design framework in sub-20nm CMOS technologies was constructed for the physical synthesis of smart memories and exploration of the huge design space. The experimental results show that customizing memory for the computerized tomography parallel back projection can achieve more than 30% area and power savings with marginal sacrifice of image accuracy.</p

Privacy Preserving Smart Metering System Based Retail Level Electricity Market

<p>Smart metering systems in distribution networks provide near real-time, two-way information exchange between end users and utilities, enabling many advanced smart grid technologies. However, the fine grained real-time data as well as the various market functionalities also pose great risks to customer privacy. In this work we propose a secure multi-party computation (SMC) based privacy preserving smart metering system. Using the proposed SMC protocol, a utility is able to perform advanced market based demand management algorithms without knowing the actual values of private end user consumption and configuration data. Using homomorphic encryption, billing is secure and verifiable. We implemented a demonstration system that includes a graphical user interface and simulates real-world network communication of the proposed SMC-enabled smart meters. The demonstration shows the feasibility of our proposed privacy preserving protocol for advanced smart grid technologies which includes load management and retail level electricity market support.</p

Secure Multiparty Computation Based Privacy Preserving Smart Metering System

Smart metering systems provide high resolution, realtime end user power consumption data for utilities to better monitor and control the system, and for end users to better manage their energy usage and bills. However, the high resolution realtime power consumption data can also be used to extract end user activity details, which could pose a great threat to user privacy. In this work, we propose a secure multi-party computation (SMC) based privacy preserving protocol for smart meter based load management. Using SMC and a proper designed electricity plan, the utility is able to perform real time demand management with individual users, without knowing the actual value of each user's consumption data. Using homomorphic encryption, the billing is secure and verifiable. We have further implemented a demonstration system which includes a graphical user interface and simulates network communication. The demonstration shows that the proposed privacy preserving protocol is feasible for implementation on commodity IT systems.</p

FFTS with near-optimal memory access through block data layouts

<p>Fast Fourier transform algorithms on large data sets achieve poor performance on various platforms because of the inefficient strided memory access patterns. These inefficient access patterns need to be reshaped to achieve high performance implementations. In this paper we formally restructure 1D, 2D and 3D FFTs targeting a generic machine model with a two-level memory hierarchy requiring block data transfers, and derive memory access pattern efficient algorithms using custom block data layouts. Using the Kronecker product formalism, we integrate our optimizations into Spiral framework. In our evaluations we demonstrate that Spiral generated hardware designs achieve close to theoretical peak performance of the targeted platform and offer significant speed-up (up to 6.5x) compared to naive baseline algorithms.</p

HAMLeT: Hardware Accelerated Memory Layout Transform within 3D-stacked DRAM

<p>Memory layout transformations via data reorganization are very common operations, which occur as a part of the computation or as a performance optimization in data-intensive applications. These operations require inefficient memory access patterns and roundtrip data movement through the memory hierarchy, failing to utilize the performance and energy-efficiency potentials of the memory subsystem. This paper proposes a high-bandwidth and energy-efficient hardware accelerated memory layout transform (HAMLeT) system integrated within a 3D-stacked DRAM. HAMLeT uses a low-overhead hardware that exploits the existing infrastructure in the logic layer of 3D-stacked DRAMs, and does not require any changes to the DRAM layers, yet it can fully exploit the locality and parallelism within the stack by implementing efficient layout transform algorithms. We analyze matrix layout transform operations (such as matrix transpose, matrix blocking and 3D matrix rotation) and demonstrate that HAMLeT can achieve close to peak system utilization, offering up to an order of magnitude performance improvement compared to the CPU and GPU memory subsystems which does not employ HAMLeT.</p