A unified framework for schedule and storage optimization.

Abstract We present a unified mathematical framework for analyzing the tradeoffs between parallelism and storage allocation within a parallelizing compiler. Using this framework, we show how to find a good storage mapping for a given schedule, a good schedule for a given storage mapping, and a good storage mapping that is valid for all legal schedules. We consider storage mappings that collapse one dimension of a multi-dimensional array, and programs that are in a single assignment form with a one-dimensional affine schedule. Our method combines affine scheduling techniques with occupancy vector analysis and incorporates general affine dependences across statements and loop nests. We formulate the constraints imposed by the data dependences and storage mappings as a set of linear inequalities, and apply numerical programming techniques to solve for the shortest occupancy vector. We consider our method to be a first step towards automating a procedure that finds the optimal tradeoff between parallelism and storage space

Mapping Stream Programs into the Compressed Domain

Due to the high data rates involved in audio, video, and signalprocessing applications, it is imperative to compress the data todecrease the amount of storage used. Unfortunately, this implies thatany program operating on the data needs to be wrapped by adecompression and re-compression stage. Re-compression can incursignificant computational overhead, while decompression swamps theapplication with the original volume of data.In this paper, we present a program transformation that greatlyaccelerates the processing of compressible data. Given a program thatoperates on uncompressed data, we output an equivalent program thatoperates directly on the compressed format. Our transformationapplies to stream programs, a restricted but useful class ofapplications with regular communication and computation patterns. Ourformulation is based on LZ77, a lossless compression algorithm that isutilized by ZIP and fully encapsulates common formats such as AppleAnimation, Microsoft RLE, and Targa.We implemented a simple subset of our techniques in the StreamItcompiler, which emits executable plugins for two popular video editingtools: MEncoder and Blender. For common operations such as coloradjustment and video compositing, mapping into the compressed domainoffers a speedup roughly proportional to the overall compressionratio. For our benchmark suite of 12 videos in Apple Animationformat, speedups range from 1.1x to 471x, with a median of 15x

Effects of wheat and oat-based whole grain foods on serum lipoprotein size and distribution in overweight middle aged people : a randomised controlled trial

Peer reviewedPublisher PD

Abstraction Layers for Scalable Microfluidic Biocomputers (Extended Version)

Microfluidic devices are emerging as an attractive technology for automatically orchestrating the reactions needed in a biological computer. Thousands of microfluidic primitives have already been integrated on a single chip, and recent trends indicate that the hardware complexity is increasing at rates comparable to Moore's Law. As in the case of silicon, it will be critical to develop abstraction layers--such as programming languages and Instruction Set Architectures (ISAs)--that decouple software development from changes in the underlying device technology.Towards this end, this paper presents BioStream, a portable language for describing biology protocols, and the Fluidic ISA, a stable interface for microfluidic chip designers. A novel algorithm translates microfluidic mixing operations from the BioStream layer to the Fluidic ISA. To demonstrate the benefits of these abstraction layers, we build two microfluidic chips that can both execute BioStream code despite significant differences at the device level. We consider this to be an important step towards building scalable biocomputers

Language and compiler support for stream programs

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 153-166).Stream programs represent an important class of high-performance computations. Defined by their regular processing of sequences of data, stream programs appear most commonly in the context of audio, video, and digital signal processing, though also in networking, encryption, and other areas. Stream programs can be naturally represented as a graph of independent actors that communicate explicitly over data channels. In this work we focus on programs where the input and output rates of actors are known at compile time, enabling aggressive transformations by the compiler; this model is known as synchronous dataflow. We develop a new programming language, StreamIt, that empowers both programmers and compiler writers to leverage the unique properties of the streaming domain. StreamIt offers several new abstractions, including hierarchical single-input single-output streams, composable primitives for data reordering, and a mechanism called teleport messaging that enables precise event handling in a distributed environment. We demonstrate the feasibility of developing applications in StreamIt via a detailed characterization of our 34,000-line benchmark suite, which spans from MPEG-2 encoding/decoding to GMTI radar processing. We also present a novel dynamic analysis for migrating legacy C programs into a streaming representation. The central premise of stream programming is that it enables the compiler to perform powerful optimizations. We support this premise by presenting a suite of new transformations. We describe the first translation of stream programs into the compressed domain, enabling programs written for uncompressed data formats to automatically operate directly on compressed data formats (based on LZ77). This technique offers a median speedup of 15x on common video editing operations.(cont.) We also review other optimizations developed in the StreamIt group, including automatic parallelization (offering an 11x mean speedup on the 16-core Raw machine), optimization of linear computations (offering a 5.5x average speedup on a Pentium 4), and cache-aware scheduling (offering a 3.5x mean speedup on a StrongARM 1100). While these transformations are beyond the reach of compilers for traditional languages such as C, they become tractable given the abundant parallelism and regular communication patterns exposed by the stream programming model.by William Thies.Ph.D

StreamIt: A Language and Compiler for Communication-Exposed Architectures

With the increasing miniaturization of transistors, wire delays are becoming a dominant factor in microprocessor performance. To address this issue, a number of emerging architectures contain replicated processing units with software-exposed communication between one unit and another (e.g., Raw, SmartMemories, TRIPS). However, for their use to be widespread, it will be necesary to develop a common machine language to allow programmers to express an algorithm in a way that can be efficiently mapped across these architectures. We propose a new common machine language for grid-based software-exposed architectures: StreamIt. StreamIt is a high-level programming language with explicit support for streaming computation. Unlike sequential programs with obscured dependence information and complex communication patterns, a stream program is naturally written as a set of concurrent filters with regular steady-state communication. The language imposes a hierarchical structure on the stream graph that enables novel representations and optimizations within the StreamIt compiler. We have implemented a fully functional compiler that parallelizes StreamIt applications for Raw, including several load-balancing transformations. Though StreamIt exposes the parallelism and communication patterns of stream programs, analysis is needed to adapt a stream program to a software-exposed processor. We describe a partitioning algorithm that employs fission and fusion transformations to adjust the granularity of a stream graph, a layout algorithm that maps a stream graph to a given network topology, and a scheduling strategy that generates a fine-grained static communication pattern for each computational element. Using the cycle-accurate Raw simulator, we demonstrate that the StreamIt compiler can automatically map a high-level stream abstraction to Raw. We consider this work to be a first step towards a portable programming model for communication-exposed architectures.Singapore-MIT Alliance (SMA

No access, no knowledge, or no interest? Examining use and non-use of assistive technologies

ABSTRACT Assistive technologies can evoke both positive and negative sentiments from users. However, much of the current literature focuses only on adoption, use, and abandonment of assistive technologies, and provides only surface-level explanations for volitional non-use. We summarize a current project that has revealed valuable insights about non-use of assistive technologies by people with visual impairments in the developing world, and discuss some of the difficulties that arise when trying to recruit non-users of assistive technology

Increasing the Reach of Snowball Sampling: The Impact of Fixed versus Lottery Incentives

ABSTRACT Though many researchers have studied how to incentivize people to respond to surveys, little is known about how these incentives impact respondents' willingness to recruit others to participate as well. In this paper, we show that the incentives offered for individual survey responses can have a dramatic impact on the overall reach of a survey through a network of peers. In a field experiment in India, we made a survey accessible via mobile phones and offered respondents either a fixed incentive (guaranteed payment of about $0.17) or a lottery incentive (1$17). When asked to choose, a significant fraction of respondents preferred the lottery incentive. However, when encouraged to spread the survey, the fixed incentive spread over 100 times further, reaching about 800 people in a day. We interpret this surprising result and discuss the implications for HCI

Learning biophysically-motivated parameters for alpha helix prediction

Background: Our goal is to develop a state-of-the-art protein secondary structure predictor, with an intuitive and biophysically-motivated energy model. We treat structure prediction as an optimization problem, using parameterizable cost functions representing biological “pseudo-energies. ” Machine learning methods are applied to estimate the values of the parameters to correctly predict known protein structures. Results: Focusing on the prediction of alpha helices in proteins, we show that a model with 302 parameters can achieve a Qα value of 77.6 % and an SOVα value of 73.4%. Such performance numbers are among the best for techniques that do not rely on external databases (such as multiple sequence alignments). Further, it is easier to extract biological significance from a model with so few parameters. Conclusions: The method presented shows promise for the prediction of protein secondary structure. Biophysically-motivated elementary free-energies can be learned using SVM techniques to construct an energy cost function whose predictive performance rivals state-of-the-art. This method is general and can be extended beyond the all-alpha case described here. 1 Backgroun