Search query: parallel performance profiling

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abstract: In the context of personalized oncology, screening for somatic tumor mutations is crucial for prediction of an individual patient's response to therapy. Massive parallel sequencing (MPS) has been suggested for routine diagnostics, but this technology has not been sufficiently evaluated with respect to feasibility, reliability, and cost effectiveness with routine diagnostic formalin fixed, paraffin embedded material. We performed ultradeep targeted semiconductor based MPS (190 amplicons covering hotspot mutations in 46 genes) in a variety of formalin fixed, paraffin embedded diagnostic samples of lung adenocarcinoma tissue with known EGFR mutations (n 28) The samples reflected the typical spectrum of tissue material for diagnostics, including small biopsies and samples with low tumor cell content. Using MPS, we successfully sequenced all samples, with a mean read depth of 2947 reads per amplicon. High quality sequence reads were obtained from samples containing =10% tumor material. In all but one sample, variant calling identified the same EGFR mutations as were detected by conventional Sanger sequencing. Moreover, we identified 43 additional mutations in 17 genes and detected amplifications in the EGFR and ERBB2 genes. MPS performance was reliable and independent of the type of material, as well as of the fixation and extraction methods, but was influenced by tumor cell content and the degree of DNA degradation. Using sample multiplexing, focused MPS approached diagnostically acceptable cost rates.
author_names: Volker Endris

Roland Penzel

Arne Warth

Alexander Muckenhuber

Peter Schirmacher

Albrecht Stenzinger

Wilko Weichert
title: Molecular diagnostic profiling of lung cancer specimens with a semiconductor based massive parallel sequencing approach: feasibility, costs, and performance compared with conventional sequencing.
venue: The Journal of molecular diagnostics JMD
year: 2013

abstract: Data stream management systems (DSMSs) are scalable, highly available, and fault tolerant systems that aggregate and analyze real time data in motion. To continuously perform analytics on the fly within the stream, state of the art DSMSs host streaming applications as a set of interconnected operators, with each operator encapsulating the semantic of a specific operation. For parallel execution on a particular platform, these operators need to be appropriately replicated in multiple instances that split and process the workload simultaneously. Because the way operators are partitioned affects the resulting performance of streaming applications, it is essential for DSMSs to have a method to compare different operators and make holistic replication decisions to avoid performance bottlenecks and resource wastage. To this end, we propose a stepwise profiling approach to optimize application performance on a given execution platform. It automatically scales distributed computations over streams based on application features and processing power of provisioned resources and builds the relationship between provisioned resources and application performance metrics to evaluate the efficiency of the resulting configuration. Experimental results confirm that the proposed approach successfully fulfills its goals with minimal profiling overhead.
author_names: Xunyun Liu

Amir Vahid Dastjerdi

Rodrigo Neves Calheiros

Chenhao Qu

Rajkumar Buyya
title: A Stepwise Auto Profiling Method for Performance Optimization of Streaming Applications
venue: ACM Trans. Auton. Adapt. Syst.
year: 2018

abstract: Abstract Typically, the execution time of a kernel on a GPU is a difficult to predict measure as it depends on a wide range of factors. Performance can be limited by either memory transfer, compute throughput or other latencies. In this paper, we improve on the roofline model following a quantitative approach and present a completely automated GPU performance prediction technique. In this respect this model utilizes micro benchmarking and profiling in a "black box" fashion as no inspection of source/binary code is required. The proposed model combines parameters in order to characterize the performance limiting factor and to estimate execution time. In addition, we propose the quadrant split visual representation, which captures the characteristics of multiple processors in relation to a particular kernel. We performed experiments on stencil computation (red/black SOR) SGEMM and a total of 28 kernels of the Rodinia benchmark suite, using six CUDA GPUs and we showed an absolute error in predictions of 27.66% in the average case. Furthermore, the performance model was also examined on an AMD GPU through the HIP programming environment. Prediction errors were comparable despite the significant architectural differences between different vendor GPUs.
author_names: Elias Konstantinidis

Yannis Cotronis
title: A quantitative roofline model for GPU kernel performance estimation using micro benchmarks and hardware metric profiling
venue: J. Parallel Distributed Comput.
year: 2017

abstract: Bile acids are important end products of cholesterol metabolism. While they have been identified as key factors in lipid emulsification and absorption due to their detergent properties, bile acids have also been shown to act as signaling molecules and intermediates between the host and the gut microbiota. To further the investigation of bile acid functions in humans, an advanced platform for high throughput analysis is essential. Herein, we describe the development and application of a 15 min UPLC procedure for the separation of bile acid species from human biofluid samples requiring minimal sample preparation. High resolution time of flight mass spectrometry was applied for profiling applications, elucidating rich bile acid profiles in both normal and disease state plasma. In parallel, a second mode of detection was developed utilizing tandem mass spectrometry for sensitive and quantitative targeted analysis of 145 bile acid (BA) species including primary, secondary, and tertiary bile acids. The latter system was validated by testing the linearity (lower limit of quantification, LLOQ, 0.25 10 nM and upper limit of quantification, ULOQ, 2.5 5 mM) precision (6.5% and accuracy (81.2 118.9% on inter and intraday analysis achieving good recovery of bile acids (serum/plasma 88% and urine 93% The ultra performance liquid chromatography mass spectrometry (UPLC MS)/MS targeted method was successfully applied to plasma, serum, and urine samples in order to compare the bile acid pool compositional difference between preprandial and postprandial states, demonstrating the utility of such analysis on human biofluids.
author_names: Magali H Sarafian

Matthew R Lewis

Alexandros Pechlivanis

Simon J L Ralphs

Mark J Mcphail

Vishal C Patel

Marc-Emmanuel Dumas

Elaine Holmes

Jeremy K Nicholson
title: Bile acid profiling and quantification in biofluids using ultra performance liquid chromatography tandem mass spectrometry.
venue: Analytical chemistry
year: 2015

abstract: MPI functions in the large scale object oriented finite element software were overloaded to carry out user defined parallel performance profiling.Log functions in Multi Processing Environment(MPE) library were inserted to MPI functions when the MPI functions were overloaded.A typical impact simulation has been carried out using 16 CPUs,and the simulation process was monitored.Parallel finite element algorithm which was used in the simulation has been evaluated by analyzing the monitor results.
author_names: Wang Hai-bing
title: Parallel performance profile of large scale object oriented finite element software
year: 2011

abstract: The paper describes a post mortem call graph profiling tool that analyses trace information generated during the execution of BSPlib programs. The purpose of the tool is to expose imbalance in either computation or communication, and to highlight portions of code that are amenable to improvement. Unlike other profiling tools, the profile information guides optimisation in an architecture independent way. From an ease of use perspective, the amount of information displayed when visualising a profile for a parallel program is no more complex than that of a sequential program.
author_names: Jonathan M D Hill

Stephen A Jarvis

Constantinos J Siniolakis

Vasil P Vasilev
title: Portable and architecture independent parallel performance tuning using a call graph profiling tool
venue: Proceedings of the Sixth Euromicro Workshop on Parallel and Distributed Processing PDP '98
year: 1998

abstract: A wide variety of large scale data have been produced in bioinformatics. In response, the need for efficient handling of biomedical big data has been partly met by parallel computing. However, the time demand of many bioinformatics programs still remains high for large scale practical uses because of factors that hinder acceleration by parallelization. Recently, new generations of storage devices have emerged, such as NAND flash based solid state drives (SSDs) and with the renewed interest in near data processing, they are increasingly becoming acceleration methods that can accompany parallel processing. In certain cases, a simple drop in replacement of hard disk drives by SSDs results in dramatic speedup. Despite the various advantages and continuous cost reduction of SSDs, there has been little review of SSD based profiling and performance exploration of important but time consuming bioinformatics programs. For an informative review, we perform in depth profiling and analysis of 23 key bioinformatics programs using multiple types of devices. Based on the insight we obtain from this research, we further discuss issues related to design and optimize bioinformatics algorithms and pipelines to fully exploit SSDs. The programs we profile cover traditional and emerging areas of importance, such as alignment, assembly, mapping, expression analysis, variant calling and metagenomics. We explain how acceleration by parallelization can be combined with SSDs for improved performance and also how using SSDs can expedite important bioinformatics pipelines, such as variant calling by the Genome Analysis Toolkit and transcriptome analysis using RNA sequencing. We hope that this review can provide useful directions and tips to accompany future bioinformatics algorithm design procedures that properly consider new generations of powerful storage devices.
author_names: Sungmin Lee

Hyeyoung Min

Sungroh Yoon
title: Will solid state drives accelerate your bioinformatics? In depth profiling, performance analysis and beyond
venue: Briefings Bioinform.
year: 2016

abstract: The PMLS parallelizing compiler for Standard ML is based upon the automatic instantiation of algorithmic skeletons at sites of higher order function (HOF) use. Rather than mechanically replacing HOFs with skeletons, which in general leads to poor parallel performance, PMLS seeks to predict run time parallel behaviour to optimise skeleton use. Static extraction of analytic cost models from programs is undecidable, and practical heuristic approaches are intractable. In contrast, PMLS utilises a hybrid approach by combining static analytic cost models for skeletons with dynamic information gathered from the sequential instrumentation of HOF argument functions. Such instrumentation is provided by an implementation independent SML interpreter, based on the language's Structural Operational Semantics (SOS) in the form of SOS rule counts. PMLS then tries to relate the rule counts to program execution times through numerical techniques. This paper considers the design and implementation of the PMLS approach to parallel performance prediction. The formulation of a general rule count cost model as a set of over determined linear equations is discussed, and their solution by single value decomposition, and by a genetic algorithm, are presented.
author_names: Norman Scaife

Greg J Michaelson

Susumu Horiguchi
title: Empirical Parallel Performance Prediction From Semantics Based Profiling
year: 2001

abstract: Large scale human microbiota studies require specimens collected from multiple sites and/or time points to maximize detection of the small effects in microbe host interactions. However, batch biases caused by experimental protocols, such as sample collection, massively parallel sequencing, and bioinformatics analyses, remain critical and should be minimized. This work evaluated the effects of preservation solutions and bacterial 16S rRNA gene primer pairs in revealing human gut microbiota composition. Since notable changes in detecting bacterial composition and abundance were observed among choice of preservatives and primer pairs, a consistent methodology is essential in minimizing their effects to facilitate comparisons between data sets. ABSTRACT Proper preservation of stool samples to minimize microbial community shifts and inactivate infectious agents is important for self collected specimens requiring shipment to laboratories when cold chain transport is not feasible. In this study, we evaluated the performance of six preservation solutions (Norgen, OMNI, RNAlater, CURNA, HEMA, and Shield) for these aspects. Following storage of human stool samples with these preservatives at room temperature for 7 days, three hypervariable regions of the bacterial 16S rRNA gene (V1 V2, V3 V4, and V4) were amplicon sequenced. We found that samples collected in two preservatives, Norgen and OMNI, showed the least shift in community composition relative to 80degC standards compared with other storage conditions, and both efficiently inhibited the growth of aerobic and anaerobic bacteria. RNAlater did not prevent bacterial activity and exhibited relatively larger community shift. Although the effect of preservation solution was small compared to intersubject variation, notable changes in microbiota composition were observed, which could create biases in downstream data analysis. When community profiles inferred from different 16S rRNA gene hypervariable regions were compared, we found differential sensitivity of primer sets in identifying overall microbial community and certain bacterial taxa. For example, reads generated by the V4 primer pair showed a higher alpha diversity of the gut microbial community. The degenerate 27f YM primer failed to detect the majority of Bifidobacteriales. Our data indicate that choice of preservation solution and 16S rRNA gene primer pair are critical determinants affecting gut microbiota profiling. IMPORTANCE Large scale human microbiota studies require specimens collected from multiple sites and/or time points to maximize detection of the small effects in microbe host interactions. However, batch biases caused by experimental protocols, such as sample collection, massively parallel sequencing, and bioinformatics analyses, remain critical and should be minimized. This work evaluated the effects of preservation solutions and bacterial 16S rRNA gene primer pairs in revealing human gut microbiota composition. Since notable changes in detecting bacterial composition and abundance were observed among choice of preservatives and primer pairs, a consistent methodology is essential in minimizing their effects to facilitate comparisons between data sets.
author_names: Zigui Chen

Pak Chun Hui

Mamie Hui

Yun Kit Yeoh

Po Yee Wong

Martin Chi-Wai Chan

Martin C S Wong

Siew Chien Ng

Francis K L Chan

Paul K S Chan
title: Impact of Preservation Method and 16S rRNA Hypervariable Region on Gut Microbiota Profiling
venue: mSystems
year: 2019

abstract: This paper presents the latest chapter in our adventures coping with a large, sequentially tuned, legacy runtime system in today's parallel world. Specifically, this paper introduces our new graphical visualizer that helps programmers understand how to program in parallel with Racket's futures and, to some extent, what performs well in sequential Racket. Overall, our experience with parallelism in Racket is that we can achieve reasonable parallel performance in Racket without sacrificing the most important property of functional programming language implementations, namely safety. That is, Racket programmers are guaranteed that every Racket primitive (and thus all functions built using Racket primitives) will either behave properly, or it will signal an error explaining what went wrong. That said, however, it is challenging to understand how to best use futures to achieve interesting speedups, and the visualizer is our attempt to more widely disseminate key performance details of the runtime system in order to help Racket programmers maximize performance.
author_names: James Swaine

Burke Fetscher

Vincent St-Amour

Robert Bruce Findler

Matthew Flatt
title: Seeing the futures: profiling shared memory parallel racket
venue: FHPC '12
year: 2012