10692830
Accurate and efficient regression modeling for microarchitectural performance and power prediction
We propose regression modeling as an efficient approach for accurately predicting performance and power for various applications executing on any microprocessor configuration in a large microarchitectural design space. This paper addresses fundamental challenges in microarchitectural simulation cost by reducing the number of required simulations and using simulated results more effectively via statistical modeling and inference.Specifically, we derive and validate regression models for performance and power. Such models enable computationally efficient statistical inference, requiring the simulation of only 1 in 5 million points of a joint microarchitecture-application design space while achieving median error rates as low as 4.1 percent for performance and 4.3 percent for power. Although both models achieve similar accuracy, the sources of accuracy are strikingly different. We present optimizations for a baseline regression model to obtain (1) application-specific models to maximize accuracy in performance prediction and (2) regional power models leveraging only the most relevant samples from the microarchitectural design space to maximize accuracy in power prediction. Assessing sensitivity to the number of samples simulated for model formulation, we find fewer than 4,000 samples from a design space of approximately 22 billion points are sufficient. Collectively, our results suggest significant potential in accurate and efficient statistical inference for microarchitectural design space exploration via regression models.
9030143
Methods of inference and learning for performance modeling of parallel applications
Increasing system and algorithmic complexity combined with a growing number of tunable application parameters pose significant challenges for analytical performance modeling. We propose a series of robust techniques to address these challenges. In particular, we apply statistical techniques such as clustering, association, and correlation analysis, to understand the application parameter space better. We construct and compare two classes of effective predictive models: piecewise polynomial regression and artifical neural networks. We compare these techniques with theoretical analyses and experimental results. Overall, both regression and neural networks are accurate with median error rates ranging from 2.2 to 10.5 percent. The comparable accuracy of these models suggest differentiating features will arise from ease of use, transparency, and computational efficiency.
9960950
Performance Optimizations and Bounds for Sparse Matrix-Vector Multiply
We consider performance tuning, by code and data structure reorganization, of sparse matrix-vector multiply (SpM×V), one of the most important computational kernels in scientific applications. This paper addresses the fundamental questions of what limits exist on such performance tuning, and how closely tuned code approaches these limits. Specifically, we develop upper and lower bounds on the performance (Mflop/s) of SpM×V when tuned using our previously proposed register blocking optimization. These bounds are based on the non-zero pattern in the matrix and the cost of basic memory operations, such as cache hits and misses. We evaluate our tuned implementations with respect to these bounds using hardware counter data on 4 different platforms and on test set of 44 sparse matrices. We find that we can often get within 20% of the upper bound, particularly on class of matrices from finite element modeling (FEM) problems; on non-FEM matrices, performance improvements of 2× are still possible. Lastly, we present new heuristic that selects optimal or near-optimal register block sizes (the key tuning parameters) more accurately than our previous heuristic. Using the new heuristic, we show improvements in SpM×V performance (Mflop/s) by as much as 2.5× over an untuned implementation. Collectively, our results suggest that future performance improvements, beyond those that we have already demonstrated for SpM×V, will come from two sources: (1) consideration of higher-level matrix structures (e.g. exploiting symmetry, matrix reordering, multiple register block sizes), and (2) optimizing kernels with more opportunity for data reuse (e.g. sparse matrix-multiple vector multiply, multiplication of AT A by a vector).
26612484
Visualizing Graphs as Trees: Plant a Seed and Watch it Grow
TreePlus is a graph browsing technique based on a tree-style layout. It shows the missing graph structure using interaction techniques and enables users to start with a specific node and incrementally explore the local structure of graphs. We believe that it supports particularly well tasks that require rapid reading of labels.
9244713
Inverse multi-objective robust evolutionary design
In this paper, we present an Inverse Multi-Objective Robust Evolutionary (IMORE) design methodology that handles the presence of uncertainty without making assumptions about the uncertainty structure. We model the clustering of uncertain events in families of nested sets using a multi-level optimization search. To reduce the high computational costs of the proposed methodology we proposed schemes for (1) adapting the step-size in estimating the uncertainty, and (2) trimming down the number of calls to the objective function in the nested search. Both offline and online adaptation strategies are considered in conjunction with the IMORE design algorithm. Design of Experiments (DOE) approaches further reduce the number of objective function calls in the online adaptive IMORE algorithm. Empirical studies conducted on a series of test functions having diverse complexities show that the proposed algorithms converge to a set of Pareto-optimal design solutions with non-dominated nominal and robustness performances efficiently.
7825415
Understanding Eight Years of InfoVis Conferences Using PaperLens
We present PaperLens, a visualization that reveals connections, trends, and activity throughout the InfoVis conference community for the last 8 years. It tightly couples views across papers, authors, and references. This paper describes how we analyzed the data, the strengths and weaknesses of PaperLens, and interesting patterns and relationships we have discovered using PaperLens.
14687618
Using a Hybrid Genetic Algorithm and Fuzzy Logic for Metabolic Modeling
The identification of metabolic systems is a complex task due to the complexity of the system and limited knowledge about the model. Mathematical equations and ODE's have been used to capture the structure of the model, and the conventional optimization techniques have been used to identify the parameters of the model. In general, however, a pure mathematical formulation of the model is difficult due to parametric uncertainty and incomplete knowledge of mechanisms. In this paper, we propose a modeling approach that (1) uses fuzzy rule-based model to augment algebraic enzyme models that are incomplete, and (2) uses a hybrid genetic algorithm to identify uncertain parameters in the model. The hybrid genetic algorithm (GA) integrates a GA with the simplex method in functional optimization to improve the GA's convergence rate. We have applied this approach to modeling the rate of three enzyme reactions in E. coli central metabolism. The proposed modeling strategy allows (1) easy incorporation of qualitative insights into a pure mathematical model and (2) adaptive identification and optimization of key parameters to fit system behaviors observed in biochemical experiments.
13264531
An efficient scattered data approximation using multilevel B-splines based on quasi-interpolants
In this paper, we propose an efficient approximation algorithm using multilevel B-splines based on quasi-interpolants. Multilevel technique uses a coarse to fine hierarchy to generate a sequence of bicubic B-spline functions whose sum approaches the desired interpolation function. To compute a set of control points, quasi-interpolants gives a procedure for deriving local spline approximation methods where a B-spline coefficient only depends on data points taken from the neighborhood of the support corresponding the B-spline. Experimental results show that the smooth surface reconstruction with high accuracy can be obtained from a selected set of scattered or dense irregular samples.