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

Visuospatial memory in dyslexia: evidence for strategic deficits.

Impairments in working memory are suggested to be one of the defining characteristics of dyslexia, and deficits in verbal recall are well documented. However, the situation regarding visuospatial memory is less clear. In a widely used measure, the Corsi blocks task, sequences of visuospatial locations can be recalled forwards, in the order presented (CF), or backwards, in reverse order (CB). Previous research has suggested that, while CF draws on spatial-sequential resources, CB may load executive and distinctly visual processes. While people with dyslexia typically show no deficit on CF, CB is rarely presented. We present three studies which indicate a consistent dyslexic deficit on CB that can be ameliorated by visual strategy instructions. Our data suggest that, without instruction, people with dyslexia are unable to adopt an effective CB strategy and this is consistent with a deficit in executive function. These results have implications for our understanding of visuospatial memory in dyslexia, and also in terms of the administration of the Corsi task to special populations

Performance Evaluation and Modeling of HPC I/O on Non-Volatile Memory

HPC applications pose high demands on I/O performance and storage capability. The emerging non-volatile memory (NVM) techniques offer low-latency, high bandwidth, and persistence for HPC applications. However, the existing I/O stack are designed and optimized based on an assumption of disk-based storage. To effectively use NVM, we must re-examine the existing high performance computing (HPC) I/O sub-system to properly integrate NVM into it. Using NVM as a fast storage, the previous assumption on the inferior performance of storage (e.g., hard drive) is not valid any more. The performance problem caused by slow storage may be mitigated; the existing mechanisms to narrow the performance gap between storage and CPU may be unnecessary and result in large overhead. Thus fully understanding the impact of introducing NVM into the HPC software stack demands a thorough performance study. In this paper, we analyze and model the performance of I/O intensive HPC applications with NVM as a block device. We study the performance from three perspectives: (1) the impact of NVM on the performance of traditional page cache; (2) a performance comparison between MPI individual I/O and POSIX I/O; and (3) the impact of NVM on the performance of collective I/O. We reveal the diminishing effects of page cache, minor performance difference between MPI individual I/O and POSIX I/O, and performance disadvantage of collective I/O on NVM due to unnecessary data shuffling. We also model the performance of MPI collective I/O and study the complex interaction between data shuffling, storage performance, and I/O access patterns.Comment: 10 page

A ferrofluid based neural network: design of an analogue associative memory

We analyse an associative memory based on a ferrofluid, consisting of a system of magnetic nano-particles suspended in a carrier fluid of variable viscosity subject to patterns of magnetic fields from an array of input and output magnetic pads. The association relies on forming patterns in the ferrofluid during a trainingdphase, in which the magnetic dipoles are free to move and rotate to minimize the total energy of the system. Once equilibrated in energy for a given input-output magnetic field pattern-pair the particles are fully or partially immobilized by cooling the carrier liquid. Thus produced particle distributions control the memory states, which are read out magnetically using spin-valve sensors incorporated in the output pads. The actual memory consists of spin distributions that is dynamic in nature, realized only in response to the input patterns that the system has been trained for. Two training algorithms for storing multiple patterns are investigated. Using Monte Carlo simulations of the physical system we demonstrate that the device is capable of storing and recalling two sets of images, each with an accuracy approaching 100%.Comment: submitted to Neural Network

Interposing Flash between Disk and DRAM to Save Energy for Streaming Workloads

In computer systems, the storage hierarchy, composed of a disk drive and a DRAM, is responsible for a large portion of the total energy consumed. This work studies the energy merit of interposing flash memory as a streaming buffer between the disk drive and the DRAM. Doing so, we extend the spin-off period of the disk drive and cut down on the DRAM capacity at the cost of (extra) flash.\ud \ud We study two different streaming applications: mobile multimedia players and media servers. Our simulated results show that for light workloads, a system with a flash as a buffer between the disk and the DRAM consumes up to 40% less energy than the same system without a flash buffer. For heavy workloads savings of at least 30% are possible. We also address the wear-out of flash and present a simple solution to extend its lifetime