Search Results for « Presto »

Datacenter networks deal with a variety of workloads, ranging from latency-sensitive small flows to bandwidth-hungry large flows. Load balancing schemes based on flow hashing, e.g., ECMP, cause congestion when hash collisions occur and can perform poorly in asymmetric topologies. Recent proposals to load balance the network require centralized traffic engineering, multipath-aware transport, or expensive specialized hardware. We propose a mechanism that avoids these limitations by (i) pushing load-balancing functionality into the soft network edge (e.g., virtual switches) such that no changes are required in the transport layer, customer VMs, or networking hardware, and (ii) load balancing on fine-grained, near-uniform units of data (flowcells) that fit within end-host segment offload optimizations used to support fast networking speeds. We design and implement such a soft-edge load balancing scheme, called Presto, and evaluate it on a 10 Gbps physical testbed. We demonstrate the computational impact of packet reordering on receivers and propose a mechanism to handle reordering in the TCP receive offload functionality. Presto's performance closely tracks that of a single, non-blocking switch over many workloads and is adaptive to failures and topology asymmetry.

UNLABELLED: Driven by dramatic technological improvements, large-scale characterization of lymphocyte receptor repertoires via high-throughput sequencing is now feasible. Although promising, the high germline and somatic diversity, especially of B-cell immunoglobulin repertoires, presents challenges for analysis requiring the development of specialized computational pipelines. We developed the REpertoire Sequencing TOolkit (pRESTO) for processing reads from high-throughput lymphocyte receptor studies. pRESTO processes raw sequences to produce error-corrected, sorted and annotated sequence sets, along with a wealth of metrics at each step. The toolkit supports multiplexed primer pools, single- or paired-end reads and emerging technologies that use single-molecule identifiers. pRESTO has been tested on data generated from Roche and Illumina platforms. It has a built-in capacity to parallelize the work between available processors and is able to efficiently process millions of sequences generated by typical high-throughput projects. AVAILABILITY AND IMPLEMENTATION: pRESTO is freely available for academic use. The software package and detailed tutorials may be downloaded from http://clip.med.yale.edu/presto.

Presto is an open-source distributed SQL query engine that supports analytics workloads involving multiple exabyte-scale data sources. Presto is used for low-latency interactive use cases as well as long-running ETL jobs at Meta. It was originally launched at Meta in 2013 and donated to the Linux Foundation in 2019. Over the last ten years, upholding query latency and scalability with the hyper growth of data volume at Meta as well as new SQL analytics requirements have raised impressive challenges for Presto. A top priority has been ensuring query reliability does not regress with the shift towards smaller, more elastic container allocation, which requires queries to run with substantially smaller memory headroom and can be preempted at any time. Additionally, new demands from machine learning, privacy, and graph analytics have driven Presto maintainers to think beyond traditional data analytics. In this paper, we discuss several successful evolutions in recent years that have improved Presto latency as well as scalability by several orders of magnitude in production at Meta. Some of the notable ones are hierarchical caching, native vectorized execution engines, materialized views, and Presto on Spark. With these new capabilities, we have deprecated or are in the process of deprecating various legacy query engines so that Presto becomes the single piece to serve interactive, ad-hoc, ETL, and graph processing workloads for the entire data warehouse.

Presto is a Lagrangian, three-dimensional explicit, transient dynamics code for the analysis of solids subjected to large, suddenly applied loads. Presto is designed for problems with large deformations, nonlinear material behavior, and contact. There is a versatile element library incorporating both continuum and structural elements. The code is designed for a parallel computing environment. This document describes the input for the code that gives users access to all of the current functionality in the code. Presto is built in an environment that allows it to be coupled with other engineering analysis codes. The input structure for the code, which uses a concept called scope, reflects the fact that Presto can be used in a coupled environment. This guide describes the scope concept and the input from the outermost to the innermost input scopes. Within a given scope, the descriptions of input commands are grouped based on code functionality. For example, all material input command lines are described in a section of the user's guide for all of the material models in the code.

PRESTO-II is a computer code developed to evaluate possible health effects from shallow land disposal of low level radioactive wastes. PRESTO-PREP is a data preprocessor that has been developed to expedite the formation of input data sets for PRESTO-II. PRESTO-PREP utilizes a library of nuclide and risk-specific data. Given an initial waste inventory, the code creates the radionuclide portion of the associated input data set for PRESTO-II. 2 references.

We describe how we extended Presto to be GPU-aware. We focus on two critical challenges: efficiently moving data from storage to GPU operators, and enabling data exchange between operators without leaving GPU memory even when a query is distributed. To guide our design, we conducted a series of initial experiments in which we executed queries derived from the TPC-H benchmark on a multi-GPU cluster using NVIDIA's C++ cuDF data-frame library, and measured how different architectures and configurations influenced performance. We show how these insights inform our extensions to Presto, detailing the architectural changes required to integrate GPU execution into the existing Presto framework. Our initial evaluation demonstrates substantial cost/performance (up to 6x) improvements over CPU Presto on standard analytical benchmarks. Our code is available as part of open-source Presto/Velox, and we have started to use it to run customer production workloads.

Abstract Summary The related Wilcoxon rank sum test and area under the receiver operator curve are ubiquitous in high dimensional biological data analysis. Current implementations do not scale readily to the increasingly large datasets generated by novel high-throughput technologies, such as single cell RNAseq. We introduce a simple and scalable implementation of both analyses, available through the R package Presto. Presto scales to big datasets, with functions optimized for both dense and sparse matrices. On a sparse dataset of 1 million observations, 10 groups, and 1,000 features, Presto performed both rank-sum and auROC analyses in only 17 seconds, compared to 6.4 hours with base R functions. Presto also includes functions to seamlessly integrate with the Seurat single cell analysis pipeline and the Bioconductor SingleCellExperiment class. Presto enables the use of robust classical analyses on big data with a simple interface and optimized implementation. Availability and Implementation Presto is available as an R package at https://github.com/immunogenomics/presto . Contact [email protected] Supplementary Information Vignettes are available with the Presto package.

BACKGROUND: There is a pressing need for new clinically feasible speech recognition tests that are theoretically motivated, sensitive to individual differences, and access the core perceptual and neurocognitive processes used in speech perception. PRESTO (Perceptually Robust English Sentence Test Open-set) is a new high-variability sentence test designed to reflect current theories of exemplar-based learning, attention, and perception, including lexical organization and automatic encoding of indexical attributes. Using sentences selected from the TIMIT (Texas Instruments/Massachusetts Institute of Technology) speech corpus, PRESTO was developed to include talker and dialect variability. The test consists of lists balanced for talker gender, keywords, frequency, and familiarity. PURPOSE: To investigate the performance, reliability, and validity of PRESTO. RESEARCH DESIGN: In Phase I, PRESTO sentences were presented in multitalker babble at four signal-to-noise ratios (SNRs) to obtain a distribution of performance. In Phase II, participants returned and were tested on new PRESTO sentences and on HINT (Hearing In Noise Test) sentences presented in multitalker babble. STUDY SAMPLE: Young, normal-hearing adults (N = 121) were recruited from the Indiana University community for Phase I. Participants who scored within the upper and lower quartiles of performance in Phase I were asked to return for Phase II (N = 40). DATA COLLECTION AND ANALYSIS: In both Phase I and Phase II, participants listened to sentences presented diotically through headphones while seated in enclosed carrels at the Speech Research Laboratory at Indiana University. They were instructed to type in the sentence that they heard using keyboards interfaced to a computer. Scoring for keywords was completed offline following data collection. Phase I data were analyzed by determining the distribution of performance on PRESTO at each SNR and at the average performance across all SNRs. PRESTO reliability was analyzed by a correlational analysis of participant performance at test (Phase I) and retest (Phase II). PRESTO validity was analyzed by a correlational analysis of participant performance on PRESTO and HINT sentences tested in Phase II, and by an analysis of variance of within-subject factors of sentence test and SNR, and a between-subjects factor of group, based on level of Phase I performance. RESULTS: A wide range of performance on PRESTO was observed; averaged across all SNRs, keyword accuracy ranged from 40.26 to 76.18% correct. PRESTO accuracy at retest (Phase II) was highly correlated with Phase I accuracy (r = 0.92, p < 0.001). PRESTO scores were also correlated with scores on HINT sentences (r = 0.52, p < 0.001). Phase II results showed an interaction between sentence test type and SNR [F(3, 114) = 121.36, p < 0.001], with better performance on HINT sentences at more favorable SNRs and better performance on PRESTO sentences at poorer SNRs. CONCLUSIONS: PRESTO demonstrated excellent test/retest reliability. Although a moderate correlation was observed between PRESTO and HINT sentences, a different pattern of results occurred with the two types of sentences depending on the level of the competition, suggesting the use of different processing strategies. Findings from this study demonstrate the importance of high-variability materials for assessing and understanding individual differences in speech perception.