5831663
Program counter-based prediction techniques for dynamic power management
Reducing energy consumption has become one of the major challenges in designing future computing systems. This paper proposes a novel idea of using program counters to predict I/O activities in the operating system. It presents a complete design of program-counter access predictor (PCAP) that dynamically learns the access patterns of applications and predicts when an I/O device can be shut down to save energy. PCAP uses path-based correlation to observe a particular sequence of program counters leading to each idle period and predicts future occurrences of that idle period. PCAP differs from previously proposed shutdown predictors in its ability to: 1) correlate I/O operations to particular behavior of the applications and users, 2) carry prediction information across multiple executions of the applications, and 3) attain higher energy savings while incurring lower mispredictions. We perform an extensive evaluation study of PCAP using a detailed trace-driven simulation and an actual Linux implementation. Our results show that PCAP achieves lower average mispredictions and higher energy savings than the simple timeout scheme and the state-of-the-art learning tree scheme
1334946
Program counter based techniques for dynamic power management
Reducing energy consumption has become one of the major challenges in designing future computing systems. We propose a novel idea of using program counters to predict I/O activities in the operating system. We present a complete design of program-counter access predictor (PCAP) that dynamically learns the access patterns of applications and predicts when an I/O device can be shut down to save energy. PCAP uses path-based correlation to observe a particular sequence of program counters leading to each idle period, and predicts future occurrences of that idle period. PCAP differs from previously proposed shutdown predictors in its ability to: (1) correlate I/O operations to particular behavior of the applications and users, (2) carry prediction information across multiple executions of the applications, and (3) attain better energy savings while incurring low mispredictions.
43789567
A Self-Organizing Flock of Condors
1998178
A Hierarchical Approach to Internet Distance Prediction
Internet distance prediction gives pair-wise latency information with limited measurements. Recent studies have revealed that the quality of existing prediction mechanisms from the application perspective is short of satisfactory. In this paper, we explore the root causes and remedies for this problem. Our experience with different landmark selection schemes shows that although selecting nearby landmarks can increase the prediction accuracy for short distances, it can cause the prediction accuracy for longer distances to degrade. Such uneven prediction quality significantly impacts application performance. Instead of trying to select the landmark nodes in some "intelligent" fashion, we propose a hierarchical prediction approach with straightforward landmark selection. Hierarchical prediction utilizes multiple coordinate sets at multiple distance scales, with the "right" scale being chosen for prediction each time. Experiments with Internet measurement datasets show that this hierarchical approach is extremely promising for increasing the accuracy of network distance prediction.
463081
Topology-Aware Routing in Structured Peer-to-Peer Overlay Networks
Structured peer-to-peer (p2p) overlay networks like CAN, Chord, Pastry and Tapestry offer a novel platform for a variety of scalable and decentralized distributed applications. They provide efficient and fault-tolerant routing, object location and load balancing within a self-organizing overlay network. One important aspect of these systems is how they exploit network proximity in the underlying Internet. We present a study of topology-aware routing approaches in p2p overlays, identify proximity neigbor selection as the most promising technique, and present an improved design in Pastry. Results obtained via analysis and via simulation of two large-scale topology models indicate that it is possible to efficiently exploit network proximity in self-organizing p2p substrates. Proximity neighbor selection incurs only a modest additional overhead for organizing and maintaining the overlay network. The resulting locality properties improve application performance and reduce network usage in the Internet substantially. Finally, we show that the impact of proximity neighbor selection on the load balancing in the p2p overlay is minimal.
481550
Improving Fine-Grained Irregular Shared-Memory Benchmarks by Data Reordering
We demonstrate that data reordering can substantially improve the performance of fine-grained irregular shared-memory benchmarks, on both hardware and software shared-memory systems. In particular, we evaluate two distinct data reordering techniques that seek to co-locate in memory objects that are in close proximity in the physical system modeled by the computation. The effects of these techniques are increased spatial locality and reduced false sharing. We evaluate the effectiveness of the data reordering techniques on a set of five irregular applications from SPLASH-2 and Chaos. We implement both techniques in a small library, allowing us to enable them in an application by adding less than 10 lines of code. Our results on one hardware and two software shared-memory systems show that, with data reordering during initialization, the performance of these applications is improved by 12%-99% on the Origin 2000, 30%-366% on TreadMarks, and 14%-269% on HLRC.
5939586
Simultaneous time slack budgeting and retiming for dual-Vdd FPGA power reduction
Field programmable dual-Vdd interconnects are effective to reduce FPGA power. Assuming uniform length interconnects, existing work has developed time slack budgeting to minimize power based on estimating the lower bound of power reduction using dual-Vdd for given time slack. In this paper, we show that such lower bound estimation cannot be extended to mixed length interconnects that are used in modern FPGAs. We develop a technique to estimate power reduction using dual-Vdd for mixed length interconnects, and apply linear programming (LP) to solve slack budgeting to minimize power for mixed length interconnects. Experiments show 53% power reduction on average compared to single-Vdd interconnects. Furthermore, this paper presents a simultaneous retiming and slack budgeting algorithm to reduce power in dual-Vdd FPGAs considering placement and flip-flop binding constraints. The algorithm is based on mixed integer and linear programming (MILP) and achieves up to 20% power reduction compared to retiming followed by slack budgeting. We propose a runtime efficient flow to apply simultaneous retiming and slack budgeting only when it is necessary. To the best of our knowledge, this paper is the first; in-depth study of simultaneous retiming and slack budgeting for dual-Vdd programmable FPGA power reduction while considering layout constraints
13831969
The Communication Performance of the Cray T3D and its Effect on Iterative Solvers
On many distributed memory systems, such as workstation clusters or the Intel iPSC/860, the multigrid algorithm suffers from having extensive communication requirements and, in general, it is not very competitive in comparison to the conjugate gradient algorithm. This is in contrast to the sequential problem whereby the multigrid algorithm is very effective in reducing the global residual, particularly for very large linear systems of equations. These two algorithms are now compared on the Cray T3D for solving very large systems of linear equations (resulted from grids of the order 2563 cells). The communication performance of the Cray T3D is first measured by the standard ping-pong tests and also by practical communication tasks that are found frequently in CFD calculations. It is found that the Cray T3D has a low latency (≈ 6 μs) and a high bandwidth interprocessor communication (120 MB/s) when the low-level intrinsic communication routines are used. As a result, the multigrid algorithm is found to be very competitive when compared with the conjugate gradient algorithm for solving the very large linear systems arising from the Direct Numerical Simulation of turbulent Combustion (DNSC). Results are contrasted by those on the Intel iPSC/860.
30629384
Ordering Unsymmetric Matrices into Bordered Block Diagonal Form for Parallel Processing
Large, sparse, unsymmetric systems of linear equations appear frequently in areas such as chemical engineering. One way of speeding up the solution of these linear systems is to solve them in parallel by reordering the unsymmetric matrix into a bordered block-diagonal (BBD) form. A multilevel ordering algorithm is presented in this paper. Numerical results of the algorithm are given to demonstrate that this algorithm gives better orderings than existing algorithms.
9078923
Robust subspace analysis for detecting visual attention regions in images
Detecting visually attentive regions of an image is a challenging but useful issue in many multimedia applications. In this paper, we describe a method to extract visual attentive regions in images using subspace estimation and analysis techniques. The image is represented in a 2D space using polar transformation of its features so that each region in the image lies in a 1D linear subspace. A new subspace estimation algorithm based on Generalized Principal Component Analysis (GPCA) is proposed. The robustness of subspace estimation is improved by using weighted least square approximation where weights are calculated from the distribution of K nearest neighbors to reduce the sensitivity of outliers. Then a new region attention measure is defined to calculate the visual attention of each region by considering both feature contrast and geometric properties of the regions. The method has been shown to be effective through experiments to be able to overcome the scale dependency of other methods. Compared with existing visual attention detection methods, it directly measures the global visual contrast at the region level as opposed to pixel level contrast and can correctly extract the attentive region.
22875771
ACO-Steiner: Ant Colony Optimization Based Rectilinear Steiner Minimal Tree Algorithm
The rectilinear Steiner minimal tree (RSMT) problem is one of the fundamental problems in physical design, especially in routing, which is known to be NP-complete. This paper presents an algorithm, called ACO-Steiner, for RSMT construction based on ant colony optimization (ACO). An RSMT is constructed with ants' movements in Hanan grid, and then the constraint of Hanan grid is broken to accelerate ants' movements to improve the performance of the algorithm. This algorithm has been implemented on a Sun workstation with Unix operating system and the results have been compared with the fastest exact RSMT algorithm, GeoSteiner 3.1 and a recent heuristic using batched greedy triple construction (BGTC). Experimental results show that ACO-Steiner can get a short running time and keep the high performance. Furthermore, it is also found that the ACO-Steiner can be easily extended to be used to some other problems, such as rectilinear Steiner minimal tree avoiding obstacles, and congestion reduction in global routing.