Lookahead Strategies for Sequential Monte Carlo

Based on the principles of importance sampling and resampling, sequential Monte Carlo (SMC) encompasses a large set of powerful techniques dealing with complex stochastic dynamic systems. Many of these systems possess strong memory, with which future information can help sharpen the inference about the current state. By providing theoretical justification of several existing algorithms and introducing several new ones, we study systematically how to construct efficient SMC algorithms to take advantage of the "future" information without creating a substantially high computational burden. The main idea is to allow for lookahead in the Monte Carlo process so that future information can be utilized in weighting and generating Monte Carlo samples, or resampling from samples of the current state.Comment: Published in at http://dx.doi.org/10.1214/12-STS401 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Arithmetic on a Distributed-Memory Quantum Multicomputer

We evaluate the performance of quantum arithmetic algorithms run on a distributed quantum computer (a quantum multicomputer). We vary the node capacity and I/O capabilities, and the network topology. The tradeoff of choosing between gates executed remotely, through ``teleported gates'' on entangled pairs of qubits (telegate), versus exchanging the relevant qubits via quantum teleportation, then executing the algorithm using local gates (teledata), is examined. We show that the teledata approach performs better, and that carry-ripple adders perform well when the teleportation block is decomposed so that the key quantum operations can be parallelized. A node size of only a few logical qubits performs adequately provided that the nodes have two transceiver qubits. A linear network topology performs acceptably for a broad range of system sizes and performance parameters. We therefore recommend pursuing small, high-I/O bandwidth nodes and a simple network. Such a machine will run Shor's algorithm for factoring large numbers efficiently.Comment: 24 pages, 10 figures, ACM transactions format. Extended version of Int. Symp. on Comp. Architecture (ISCA) paper; v2, correct one circuit error, numerous small changes for clarity, add reference

Enabling Cross-Event Optimization in Discrete-Event Simulation Through Compile-Time Event Batching

A discrete-event simulation (DES) involves the execution of a sequence of event handlers dynamically scheduled at runtime. As a consequence, a priori knowledge of the control flow of the overall simulation program is limited. In particular, powerful optimizations supported by modern compilers can only be applied on the scope of individual event handlers, which frequently involve only a few lines of code. We propose a method that extends the scope for compiler optimizations in discrete-event simulations by generating batches of multiple events that are subjected to compiler optimizations as contiguous procedures. A runtime mechanism executes suitable batches at negligible overhead. Our method does not require any compiler extensions and introduces only minor additional effort during model development. The feasibility and potential performance gains of the approach are illustrated on the example of an idealized proof-ofconcept model. We believe that the applicability of the approach extends to general event-driven programs

Risk Limiting Dispatch with Ramping Constraints

Reliable operation in power systems is becoming more difficult as the penetration of random renewable resources increases. In particular, operators face the risk of not scheduling enough traditional generators in the times when renewable energies becomes lower than expected. In this paper we study the optimal trade-off between system and risk, and the cost of scheduling reserve generators. We explicitly model the ramping constraints on the generators. We model the problem as a multi-period stochastic control problem, and we show the structure of the optimal dispatch. We then show how to efficiently compute the dispatch using two methods: i) solving a surrogate chance constrained program, ii) a MPC-type look ahead controller. Using real world data, we show the chance constrained dispatch outperforms the MPC controller and is also robust to changes in the probability distribution of the renewables.Comment: Shorter version submitted to smartgrid comm 201