Characterizing the performance of Flash memory storage devices and its impact on algorithm design

Initially used in digital audio players, digital cameras, mobile phones, and USB memory sticks, flash memory may become the dominant form of end-user storage in mobile computing, either completely replacing the magnetic hard disks or being an additional secondary storage. We study the design of algorithms and data structures that can exploit the flash memory devices better. For this, we characterize the performance of NAND flash based storage devices, including many solid state disks. We show that these devices have better random read performance than hard disks, but much worse random write performance. We also analyze the effect of misalignments, aging and past I/O patterns etc. on the performance obtained on these devices. We show that despite the similarities between flash memory and RAM (fast random reads) and between flash disk and hard disk (both are block based devices), the algorithms designed in the RAM model or the external memory model do not realize the full potential of the flash memory devices. We later give some broad guidelines for designing algorithms which can exploit the comparative advantages of both a flash memory device and a hard disk, when used together

uFLIP: Understanding Flash IO Patterns

Does the advent of flash devices constitute a radical change for secondary storage? How should database systems adapt to this new form of secondary storage? Before we can answer these questions, we need to fully understand the performance characteristics of flash devices. More specifically, we want to establish what kind of IOs should be favored (or avoided) when designing algorithms and architectures for flash-based systems. In this paper, we focus on flash IO patterns, that capture relevant distribution of IOs in time and space, and our goal is to quantify their performance. We define uFLIP, a benchmark for measuring the response time of flash IO patterns. We also present a benchmarking methodology which takes into account the particular characteristics of flash devices. Finally, we present the results obtained by measuring eleven flash devices, and derive a set of design hints that should drive the development of flash-based systems on current devices.Comment: CIDR 200

Shape Complexity from Image Similarity

We present an approach to automatically compute the complexity of a given 3D shape. Previous approaches have made use of geometric and/or topological properties of the 3D shape to compute complexity. Our approach is based on shape appearance and estimates the complexity of a given 3D shape according to how 2D views of the shape diverge from each other. We use similarity among views of the 3D shape as the basis for our complexity computation. Hence our approach uses claims from psychology that humans mentally represent 3D shapes as organizations of 2D views and, therefore, mimics how humans gauge shape complexity. Experimental results show that our approach produces results that are more in agreement with the human notion of shape complexity than those obtained using previous approaches

Maximum Cardinality Popular Matchings in Strict Two-sided Preference Lists

We consider the problem of computing a maximum cardinality {\em popular} matching in a bipartite graph G = (\A\cup\B, E) where each vertex u \in \A\cup\B ranks its neighbors in a strict order of preference. This is the same as an instance of the {\em stable marriage} problem with incomplete lists. A matching $M^*$ is said to be popular if there is no matching $M$ such that more vertices are better off in $M$ than in $M^*$. \smallskip Popular matchings have been extensively studied in the case of one-sided preference lists, i.e., only vertices of \A have preferences over their neighbors while vertices in \B have no preferences; polynomial time algorithms have been shown here to determine if a given instance admits a popular matching or not and if so, to compute one with maximum cardinality. It has very recently been shown that for two-sided preference lists, the problem of determining if a given instance admits a popular matching or not is NP-complete. However this hardness result assumes that preference lists have {\em ties}. When preference lists are {\em strict}, it is easy to show that popular matchings always exist since stable matchings always exist and they are popular. But the complexity of computing a maximum cardinality popular matching was unknown. In this paper we show an $O(mn)$ algorithm for this problem, where n = |\A| + |\B| and $m = |E|$

New Results for Non-preemptive Speed Scaling

We consider the speed scaling problem introduced in the seminal paper of Yao et al.. In this problem, a number of jobs, each with its own processing volume, release time, and deadline needs to be executed on a speed-scalable processor. The power consumption of this processor is $P(s) = s^\alpha$, where $s$ is the processing speed, and $\alpha > 1$ is a constant. The total energy consumption is power integrated over time, and the goal is to process all jobs while minimizing the energy consumption. The preemptive version of the problem, along with its many variants, has been extensively studied over the years. However, little is known about the non-preemptive version of the problem, except that it is strongly NP-hard and allows a constant factor approximation. Up until now, the (general) complexity of this problem is unknown. In the present paper, we study an important special case of the problem, where the job intervals form a laminar family, and present a quasipolynomial-time approximation scheme for it, thereby showing that (at least) this special case is not APX-hard, unless $NP \subseteq DTIME(2^{poly(\log n)})$. The second contribution of this work is a polynomial-time algorithm for the special case of equal-volume jobs, where previously only a $2^\alpha$ approximation was known. In addition, we show that two other special cases of this problem allow fully polynomial-time approximation schemes (FPTASs)

Symmetry Detection in Large Scale City Scans

In this report we present a novel method for detecting partial symmetries in very large point clouds of 3D city scans. Unlike previous work, which was limited to data sets of a few hundred megabytes maximum, our method scales to very large scenes. We map the detection problem to a nearestneighbor search in a low-dimensional feature space, followed by a cascade of tests for geometric clustering of potential matches. Our algorithm robustly handles noisy real-world scanner data, obtaining a recognition performance comparable to state-of-the-art methods. In practice, it scales linearly with the scene size and achieves a high absolute throughput, processing half a terabyte of raw scanner data over night on a dual socket commodity PC

Acquisition and analysis of bispectral bidirectional reflectance distribution functions

In fluorescent materials, energy from a certain band of incident wavelengths is reflected or reradiated at larger wavelengths, i.e. with lower energy per photon. While fluorescent materials are common in everyday life, they have received little attention in computer graphics. Especially, no bidirectional reflectance measurements of fluorescent materials have been available so far. In this paper, we develop the concept of a bispectral BRDF, which extends the well-known concept of the bidirectional reflectance distribution function (BRDF) to account for energy transfer between wavelengths. Using a bidirectional and bispectral measurement setup, we acquire reflectance data of a variety of fluorescent materials, including vehicle paints, paper and fabric. We show bispectral renderings of the measured data and compare them with reduced versions of the bispectral BRDF, including the traditional RGB vector valued BRDF. Principal component analysis of the measured data reveals that for some materials the fluorescent reradiation spectrum changes considerably over the range of directions. We further show that bispectral BRDFs can be efficiently acquired using an acquisition strategy based on principal components