Parallel application signature for performance analysis and prediction

•

0 likes•31 views

The document describes a method called PAS2P that was developed to characterize and predict the performance of parallel scientific applications on different hardware. PAS2P identifies representative message-passing phases within applications to create a "signature" that enables predicting execution times with over 97% accuracy based on tests of various applications run on different clusters.

Education

Parallel Application Signature for Performance Analysis and Prediction
Abstract:
Predicting the performance of parallel scientific applications is becoming
increasingly complex. Our goal was to characterize the behavior of
message-passing applications on different target machines. To achieve this
goal, we developed a method called parallel application signature for
performance prediction (PAS2P), which strives to describe an application
based on its behavior. Based on the application’s message-passing activity,
we identified and extracted representative phases, with which we created
a parallel application signature that enabled us to predict the application’s
performance. We experimented with using different scientific applications
on different clusters. We were able to predict execution times with an
average accuracy greater than 97 percent.
Hardware Requirements:
• System : Pentium IV 2.4 GHz.
• Hard Disk ` : 40 GB.
• Floppy Drive : 1.44 Mb.

• Monitor : 15 VGA Colour.
• Mouse : Logitech.
• RAM : 256 Mb.
Software Requirements:
• Operating system : - Windows XP.
• Front End : - JSP
• Back End : - SQL Server
Software Requirements:
• Operating system : - Windows XP.
• Front End : - .Net
• Back End : - SQL Server

What's hot

Unlock the value of your big data infrastructure

ManageEngine, Zoho Corporation

Hands On With Spark: Creating A Fast Data Pipeline With Structured Streaming ...

Lightbend

Connect at Twitter-scale | Jordan Bull and Ryanne Dolan, Twitter

HostedbyConfluent

NGINX for Application Delivery & Acceleration

NGINX, Inc.

Evgeniy Burak (HYS Enterprise): “Spring Data REST or intellectual job VS manual”

HYS Enterprise

Instruments to play microservice

Chandresh Pancholi

Reactive Streams 1.0.0 is now live, and so are our implementations in Akka Streams 1.0 and Slick 3.0. Reactive Streams is an engineering collaboration between heavy hitters in the area of streaming data on the JVM. With the Reactive Streams Special Interest Group, we set out to standardize a common ground for achieving statically-typed, high-performance, low latency, asynchronous streams of data with built-in non-blocking back pressure—with the goal of creating a vibrant ecosystem of interoperating implementations, and with a vision of one day making it into a future version of Java. Akka (recent winner of “Most Innovative Open Source Tech in 2015”) is a toolkit for building message-driven applications. With Akka Streams 1.0, Akka has incorporated a graphical DSL for composing data streams, an execution model that decouples the stream’s staged computation—it’s “blueprint”—from its execution (allowing for actor-based, single-threaded and fully distributed and clustered execution), type safe stream composition, an implementation of the Reactive Streaming specification that enables back-pressure, and more than 20 predefined stream “processing stages” that provide common streaming transformations that developers can tap into (for splitting streams, transforming streams, merging streams, and more). Slick is a relational database query and access library for Scala that enables loose-coupling, minimal configuration requirements and abstraction of the complexities of connecting with relational databases. With Slick 3.0, Slick now supports the Reactive Streams API for providing asynchronous stream processing with non-blocking back-pressure. Slick 3.0 also allows elegant mapping across multiple data types, static verification and type inference for embedded SQL statements, compile-time error discovery, and JDBC support for interoperability with all existing drivers.

A Deeper Look Into Reactive Streams with Akka Streams 1.0 and Slick 3.0

Legacy Typesafe (now Lightbend)

Zabbix Monitoring Platform

Seyedmajid Etehadi

At Pinterest, hundreds of services and third-party tools that are implemented in various programming languages generate billions of events every day. To achieve scalable and reliable low latency logging, there are several challenges: (1) uploading logs that are generated in various formats from tens of thousands of hosts to Kafka in a timely manner; (2) running Kafka reliably on Amazon Web Services where the virtual instances are less reliable than on-premises hardware; (3) moving tens of terabytes data per day from Kafka to cloud storage reliably and efficiently, and guaranteeing exact one time persistence per message. In this talk, we will present Pinterest’s logging pipeline, and share our experience addressing these challenges. We will dive deep into the three components we developed: data uploading from service hosts to Kafka, data transportation from Kafka to S3, and data sanitization. We will also share our experience in operating Kafka at scale in the cloud.

Scalable and Reliable Logging at Pinterest

Krishna Gade

Kafka - Linkedin's messaging backbone

Ayyappadas Ravindran (Appu)

Presented at ServerlessConf NYC 2016. This presentation will discuss the experiences of a skilled, enterprise, J2EE team moving to Amazon Web Services to build a new “serverless” solution. This will include motivation for choosing and experience using specific technology (Java, Lambda, S3, RDS, API Gateway, VPC, …) The talk will qualitatively explain the productivity improvement achieved by going “serverless” relative to a more traditional application server design. We will also identify the top three helpful technologies, the three biggest hurdles and our wish list for three new capabilities.

Donald Ferguson - Old Programmers Can Learn New Tricks

ServerlessConf

ANTON BOYKO, Founder and main speaker of Ukrainian Microsoft Azure Community. When many people hear the word microservices, they think of Kubernetes. And it’s hard to blame them: the utilization of Docker containerization together with Kubernetes orchestration really seems like a match made in heaven. But, there is a new name in town: serverless (or FaaS). It seems it can offer you all the stuff that Kubernetes has and more. Native support for .NET, Java, Python, Node. Out-of-the-box binding for things like HTTP incoming requests (for Web API), incoming queue message (for processing async tasks), time-based trigger, etc. It also comes with micro billing (you pay only for execution time, not for idle time) and 0-to-infinite scalability. But is it all too good to be true? Aren't there any drawbacks? Is it all a huge marketing scheme to make us actually pay more? Let's find out.

Azure Functions - the evolution of microservices platform or marketing gibber...

Katherine Golovinova

Advanced Spark Meetup - Jan 12, 2016

Michelle Casbon

As a streaming platform, Apache Kafka provides low-latency, high-throughput, fault-tolerant publish and subscribe pipelines and excels at processing streams of real-time events. Kafka provides reliable, millisecond delivery for connecting downstream systems with real-time data. In this talk, we will show how easy it is to leverage Kafka and the Elasticsearch connector to keep your indices populated with the latest data from the rest of your enterprise, as it changes.

Integrating Apache Kafka and Elastic Using the Connect Framework

confluent

In this session, we will discuss Live Aggregators (LA), Mist’s highly reliable and massively scalable in-house real time aggregation system that relies on Kafka for ensuring fault tolerance and scalability. LA consumes billions of messages a day from Kafka with a memory footprint of over 750 GB and aggregates over 100 million timeseries. Since it runs entirely on top of AWS spot instances, it is designed to be highly reliable. LA can recover from hours long complete EC2 outages using its checkpointing mechanism that depends on Kafka. This recovery mechanism recovers the checkpoint and replays messages from Kafka where it left off, ensuring no data loss. The characteristic that sets LA apart is its ability to autoscale by intelligently learning about resource usage and allocating resources accordingly. LA emits custom metrics that track resource usage for different components, i.e., Kafka consumer, shared memory manager and aggregator, to achieve server utilization of over 70%. We do multi-level aggregations in LA to intelligently solve load imbalance issues amongst different partitions for a Kafka topic. We’d demonstrate multi-level aggregation using an example in which we aggregate indoor location data coming from different organizations both spatially and temporally. We’d explain how changing partitioning key, along with writing intermediate data back to Kafka in a new topic for the next level aggregators helps Mist scale our solution. LA runs on top of 400+ cores, comprised of 10+ different Amazon EC2 spot instance types/sizes. We track the CPU usage for reading each Kafka stream on all the different instance types/sizes. We have several months of such data from our production Mesos cluster, which we are incorporating into LA’s scheduler to improve our server utilization and avoid CPU hot spots from developing on our cluster. Detailed Blog:https://www.mist.com/live-aggregators-highly-reliable-massively-scalable-real-time-aggregation-system/

Cost Effectively and Reliably Aggregating Billions of Messages Per Day Using ...

confluent

Azkaban

Anatoliy Nikulin

Rob Gruhl and Erik Erikson - What We Learned in 18 Serverless Months at Nords...

ServerlessConf

Key to optimal end user experience

ManageEngine, Zoho Corporation

The GUI, calling it our mortal enemy would probably be a bit too far. There are some obvious use-cases in which using a GUI to discover new functionality and to get acquainted to a new product would be a solid approach. In this session, we will do exactly that, we will setup a number of different components on a server while using PowerShell logging to record what is happening on the background. We will analyze these logs and retrieve a step-by-step playbook to reproduce the configuration.

Breakdown the GUI - PowerShell logging to automate everything

Jaap Brasser

What's hot (19)

Unlock the value of your big data infrastructure

Hands On With Spark: Creating A Fast Data Pipeline With Structured Streaming ...

Connect at Twitter-scale | Jordan Bull and Ryanne Dolan, Twitter

NGINX for Application Delivery & Acceleration

Evgeniy Burak (HYS Enterprise): “Spring Data REST or intellectual job VS manual”

Instruments to play microservice

A Deeper Look Into Reactive Streams with Akka Streams 1.0 and Slick 3.0

Zabbix Monitoring Platform

Scalable and Reliable Logging at Pinterest

Kafka - Linkedin's messaging backbone

Donald Ferguson - Old Programmers Can Learn New Tricks

Azure Functions - the evolution of microservices platform or marketing gibber...

Advanced Spark Meetup - Jan 12, 2016

Integrating Apache Kafka and Elastic Using the Connect Framework

Cost Effectively and Reliably Aggregating Billions of Messages Per Day Using ...

Azkaban

Rob Gruhl and Erik Erikson - What We Learned in 18 Serverless Months at Nords...

Key to optimal end user experience

Breakdown the GUI - PowerShell logging to automate everything

Viewers also liked

Vem ska betala för vägslitaget - Vägen till trafiksäker framkomlighet

Johan Granlund

Performancepredictionforsoftwarearchitectures 100810045752-phpapp02

NNfamily

New Taylor Swift Album Leaked On Internet In Advance Of Tomorrow’s Release

magicaltenant8123

Karnal

publicnoticeads

LETRAMENTO MIDIÁTICO: Rádio

Hercules Santos

LE PRÉSENT DE L’INDICATIFVERBES RÉGULIERS EN -ER (O presente dos verbos regu...

# Thatpotatomine

Instrumentos de cuerda frotada - claqué

alipica

Pemeriksaan Lengkap Genetalia Wanita dan Pria

nanda yudip

Introduction to parallel_computing

Mehul Patel

Parallel computing chapter 3

Md. Mahedi Mahfuj

Parallel computing

virend111

St sev 4722 84

Angelo Ratliff

QNT 351 COMPLETE CLASS

hw soloutions

Viewers also liked (13)

Vem ska betala för vägslitaget - Vägen till trafiksäker framkomlighet

Performancepredictionforsoftwarearchitectures 100810045752-phpapp02

New Taylor Swift Album Leaked On Internet In Advance Of Tomorrow’s Release

Karnal

LETRAMENTO MIDIÁTICO: Rádio

LE PRÉSENT DE L’INDICATIFVERBES RÉGULIERS EN -ER (O presente dos verbos regu...

Instrumentos de cuerda frotada - claqué

Pemeriksaan Lengkap Genetalia Wanita dan Pria

Introduction to parallel_computing

Parallel computing chapter 3

Parallel computing

St sev 4722 84

QNT 351 COMPLETE CLASS

Similar to Parallel application signature for performance analysis and prediction

Himansu-Java&BigdataDeveloper

Himansu Behera

Nmap

Sreekanth Narendran

Spark Uber Development Kit

DataWorks Summit/Hadoop Summit

Heterogeneous cloud framework for big data genome sequencing

ieeepondy

Galaxy

bosc

Boosting spark performance: An Overview of Techniques

Ahsan Javed Awan

Developing high frequency indicators using real time tick data on apache supe...

Zekeriya Besiroglu

Spark 1.0

Jatin Arora

Java is one of the most popular languages and it's very important to understand the performance of Java servers. Modern JVMs compile the Java code in runtime using Just-In-Time (JIT) compiler and such JIT compiled code runs very close to optimized native code in terms of speed. When understanding performance, it's important to know how Java works and we can also measure the performance using key metrics like Throughput and Latency. After measuring the performance, we can use profilers to understand the application behavior and find performance bottlenecks. In this session, we will look at how Java manages the memory and how it optimizes the Java code using JIT compilation. We will also look at how we can use the Java Flight Recorder (JFR) to profile the JVM and find performance bottlenecks. Finally, we can look at how "Flame Graphs" can be used to identify the most frequent code-paths quickly and accurately.

Java Performance and Profiling

WSO2

Practical virtual network functions with Snabb (SDN Barcelona VI)

Igalia

Apache edgent

Soma Shekarchi

As spark applications move to a containerized environment, there are many questions about how to best configure server systems in the container world. In this talk we will demonstrate a set of tools to better monitor performance and identify optimal configuration settings. We will demonstrate how Prometheus, a project that is now part of the Cloud Native Computing Foundation (CNCF), can be applied to monitor and archive system performance data in a containerized spark environment. In our examples, we will gather spark metric output through Prometheus and present the data with Grafana dashboards. We will use our examples to demonstrate how performance can be enhanced through different tuned configuration settings. Our demo will show how to configure settings across the cluster as well as within each node.

SCALABLE MONITORING USING PROMETHEUS WITH APACHE SPARK

zmhassan

As Apache Spark applications move to a containerized environment, there are many questions about how to best configure server systems in the container world. In this talk we will demonstrate a set of tools to better monitor performance and identify optimal configuration settings. We will demonstrate how Prometheus, a project that is now part of the Cloud Native Computing Foundation (CNCF: https://www.cncf.io/projects/), can be applied to monitor and archive system performance data in a containerized spark environment. In our examples, we will gather spark metric output through Prometheus and present the data with Grafana dashboards. We will use our examples to demonstrate how performance can be enhanced through different tuned configuration settings. Our demo will show how to configure settings across the cluster as well as within each node.

Scalable Monitoring Using Prometheus with Apache Spark Clusters with Diane F...

Databricks

Building an open source high performance data analytics platform

supun06

Presented by Eren Avsarogullari and Pavel Hardak (ApacheCon 2020) https://www.linkedin.com/in/erenavsarogullari/ https://www.linkedin.com/in/pavelhardak/ Apache Spark is the backbone of Workday's Prism Analytics Platform, supporting various data processing use-cases such as Data Ingestion, Preparation(Cleaning, Transformation & Publishing) and Discovery. At Workday, we extend Spark OSS repo and build custom Spark releases covering our custom patches on the top of Spark OSS patches. Custom Spark release development introduces the challenges when supporting multiple Spark versions against to a single repo and dealing with large numbers of customers, each of which can execute their own long-running Spark Applications. When building the custom Spark releases and new Spark features, dedicated Benchmark pipeline is also important to catch performance regression by running the standard TPC-H & TPC-DS queries against to both Spark versions and monitoring Spark driver & executors' runtime behaviors before production. At deployment phase, we also follow progressive roll-out plan leveraged by Feature Toggles used to enable/disable the new Spark features at the runtime. As part of our development lifecycle, Feature Toggles help on various use cases such as selection of Spark compile-time and runtime versions, running test pipelines against to both Spark versions on the build pipeline and supporting progressive roll-out deployment when dealing with large numbers of customers and long-running Spark Applications. On the other hand, executed Spark queries' operation level runtime behaviors are important for debugging and troubleshooting. Incoming Spark release is going to introduce new SQL Rest API exposing executed queries' operation level runtime metrics and we transform them to queryable Hive tables in order to track operation level runtime behaviors per executed query. In the light of these, this session aims to cover Spark feature development lifecycle at Workday by covering custom Spark Upgrade model, Benchmark & Monitoring Pipeline and Spark Runtime Metrics Pipeline details through used patterns and technologies step by step.

Spark Development Lifecycle at Workday - ApacheCon 2020

Pavel Hardak

Presented by Pavel Hardak and Eren Avsarogullari (ApacheCon 2020) https://www.linkedin.com/in/pavelhardak/ https://www.linkedin.com/in/erenavsarogullari/ Title: Apache Spark Development Lifecycle at Workday Abstract: Apache Spark is the backbone of Workday's Prism Analytics Platform, supporting various data processing use-cases such as Data Ingestion, Preparation(Cleaning, Transformation & Publishing) and Discovery. At Workday, we extend Spark OSS repo and build custom Spark releases covering our custom patches on the top of Spark OSS patches. Custom Spark release development introduces the challenges when supporting multiple Spark versions against to a single repo and dealing with large numbers of customers, each of which can execute their own long-running Spark Applications. When building the custom Spark releases and new Spark features, dedicated Benchmark pipeline is also important to catch performance regression by running the standard TPC-H & TPC-DS queries against to both Spark versions and monitoring Spark driver & executors' runtime behaviors before production. At deployment phase, we also follow progressive roll-out plan leveraged by Feature Toggles used to enable/disable the new Spark features at the runtime. As part of our development lifecycle, Feature Toggles help on various use cases such as selection of Spark compile-time and runtime versions, running test pipelines against to both Spark versions on the build pipeline and supporting progressive roll-out deployment when dealing with large numbers of customers and long-running Spark Applications. On the other hand, executed Spark queries' operation level runtime behaviors are important for debugging and troubleshooting. Incoming Spark release is going to introduce new SQL Rest API exposing executed queries' operation level runtime metrics and we transform them to queryable Hive tables in order to track operation level runtime behaviors per executed query. In the light of these, this session aims to cover Spark feature development lifecycle at Workday by covering custom Spark Upgrade model, Benchmark & Monitoring Pipeline and Spark Runtime Metrics Pipeline details through used patterns and technologies step by step.

Apache Spark Development Lifecycle @ Workday - ApacheCon 2020

Eren Avşaroğulları

Pegasus-Poster-2016-final-v2

Samrat Jha

Java Performance & Profiling

Isuru Perera

Seattle Spark Meetup Mobius CSharp API

shareddatamsft

Graphical packet generator

tusharjadhav2611

Similar to Parallel application signature for performance analysis and prediction (20)

Himansu-Java&BigdataDeveloper

Nmap

Spark Uber Development Kit

Heterogeneous cloud framework for big data genome sequencing

Galaxy

Boosting spark performance: An Overview of Techniques

Developing high frequency indicators using real time tick data on apache supe...

Spark 1.0

Java Performance and Profiling

Practical virtual network functions with Snabb (SDN Barcelona VI)

Apache edgent

SCALABLE MONITORING USING PROMETHEUS WITH APACHE SPARK

Scalable Monitoring Using Prometheus with Apache Spark Clusters with Diane F...

Building an open source high performance data analytics platform

Spark Development Lifecycle at Workday - ApacheCon 2020

Apache Spark Development Lifecycle @ Workday - ApacheCon 2020

Pegasus-Poster-2016-final-v2

Java Performance & Profiling

Seattle Spark Meetup Mobius CSharp API

Graphical packet generator

More from ieeeprojectschennai

Parallel application signature for performance analysis and prediction +91-9994232214,8144199666, ieeeprojectchennai@gmail.com, www.projectsieee.com, www.ieee-projects-chennai.com IEEE PROJECTS 2015-2016 ----------------------------------- Contact:+91-9994232214,+91-8144199666 Email:ieeeprojectchennai@gmail.com Support: ------------- Projects Code Documentation PPT Projects Video File Projects Explanation Teamviewer Support

Parallel application signature for performance analysis and prediction

ieeeprojectschennai

Dynamic scheduling of irregular stream programs toward many core scalability +91-9994232214,8144199666, ieeeprojectchennai@gmail.com, www.projectsieee.com, www.ieee-projects-chennai.com IEEE PROJECTS 2015-2016 ----------------------------------- Contact:+91-9994232214,+91-8144199666 Email:ieeeprojectchennai@gmail.com Support: ------------- Projects Code Documentation PPT Projects Video File Projects Explanation Teamviewer Support

Dynamic scheduling of irregular stream programs toward many core scalability

ieeeprojectschennai

Swiper exploiting virtual machine vulnerability in third party clouds with competition for io resources +91-9994232214,8144199666, ieeeprojectchennai@gmail.com, www.projectsieee.com, www.ieee-projects-chennai.com IEEE PROJECTS 2015-2016 ----------------------------------- Contact:+91-9994232214,+91-8144199666 Email:ieeeprojectchennai@gmail.com Support: ------------- Projects Code Documentation PPT Projects Video File Projects Explanation Teamviewer Support

Swiper exploiting virtual machine vulnerability in third party clouds with co...

ieeeprojectschennai

Dynamic scheduling of irregular stream programs toward many core scalability

ieeeprojectschennai

Swiper exploiting virtual machine vulnerability in third party clouds with co...

ieeeprojectschennai

Practical conflict graphs in the wild

ieeeprojectschennai

Design and analysis of medium access protocol throughput and short term fairness perspective +91-9994232214,8144199666, ieeeprojectchennai@gmail.com, www.projectsieee.com, www.ieee-projects-chennai.com IEEE PROJECTS 2015-2016 ----------------------------------- Contact:+91-9994232214,+91-8144199666 Email:ieeeprojectchennai@gmail.com Support: ------------- Projects Code Documentation PPT Projects Video File Projects Explanation Teamviewer Support

Design and analysis of medium access protocol throughput and short term fairn...

ieeeprojectschennai

Fundamental limits of cdf based scheduling throughput, fairness, and feedback overhead +91-9994232214,8144199666, ieeeprojectchennai@gmail.com, www.projectsieee.com, www.ieee-projects-chennai.com IEEE PROJECTS 2015-2016 ----------------------------------- Contact:+91-9994232214,+91-8144199666 Email:ieeeprojectchennai@gmail.com Support: ------------- Projects Code Documentation PPT Projects Video File Projects Explanation Teamviewer Support

Fundamental limits of cdf based scheduling throughput, fairness, and feedback...

ieeeprojectschennai

Ensuring predictable contact opportunity for scalable vehicular internet access on the go +91-9994232214,8144199666, ieeeprojectchennai@gmail.com, www.projectsieee.com, www.ieee-projects-chennai.com IEEE PROJECTS 2015-2016 ----------------------------------- Contact:+91-9994232214,+91-8144199666 Email:ieeeprojectchennai@gmail.com Support: ------------- Projects Code Documentation PPT Projects Video File Projects Explanation Teamviewer Support

Ensuring predictable contact opportunity for scalable vehicular internet acce...

ieeeprojectschennai

Utilize signal traces from others a crowdsourcing perspective of energy saving in cellular data communication +91-9994232214,8144199666, ieeeprojectchennai@gmail.com, www.projectsieee.com, www.ieee-projects-chennai.com IEEE PROJECTS 2015-2016 ----------------------------------- Contact:+91-9994232214,+91-8144199666 Email:ieeeprojectchennai@gmail.com Support: ------------- Projects Code Documentation PPT Projects Video File Projects Explanation Teamviewer Support

Utilize signal traces from others a crowdsourcing perspective of energy savin...

ieeeprojectschennai

Swimming seamless and efficient wi fi based internet access from moving vehicles +91-9994232214,8144199666, ieeeprojectchennai@gmail.com, www.projectsieee.com, www.ieee-projects-chennai.com IEEE PROJECTS 2015-2016 ----------------------------------- Contact:+91-9994232214,+91-8144199666 Email:ieeeprojectchennai@gmail.com Support: ------------- Projects Code Documentation PPT Projects Video File Projects Explanation Teamviewer Support

Swimming seamless and efficient wi fi based internet access from moving vehicles

ieeeprojectschennai

Spatial spectrum access game

ieeeprojectschennai

On availability performability tradeoff in wireless mesh networks +91-9994232214,8144199666, ieeeprojectchennai@gmail.com, www.projectsieee.com, www.ieee-projects-chennai.com IEEE PROJECTS 2015-2016 ----------------------------------- Contact:+91-9994232214,+91-8144199666 Email:ieeeprojectchennai@gmail.com Support: ------------- Projects Code Documentation PPT Projects Video File Projects Explanation Teamviewer Support

On availability performability tradeoff in wireless mesh networks

ieeeprojectschennai

Managing contention with medley

ieeeprojectschennai

Impact of location popularity on throughput and delay in mobile ad hoc networks +91-9994232214,8144199666, ieeeprojectchennai@gmail.com, www.projectsieee.com, www.ieee-projects-chennai.com IEEE PROJECTS 2015-2016 ----------------------------------- Contact:+91-9994232214,+91-8144199666 Email:ieeeprojectchennai@gmail.com Support: ------------- Projects Code Documentation PPT Projects Video File Projects Explanation Teamviewer Support

Impact of location popularity on throughput and delay in mobile ad hoc networks

ieeeprojectschennai

Frequency diversity aware wi-fi using ofdm-based bloom filters +91-9994232214,8144199666, ieeeprojectchennai@gmail.com, www.projectsieee.com, www.ieee-projects-chennai.com IEEE PROJECTS 2015-2016 ----------------------------------- Contact:+91-9994232214,+91-8144199666 Email:ieeeprojectchennai@gmail.com Support: ------------- Projects Code Documentation PPT Projects Video File Projects Explanation Teamviewer Support

Frequency diversity aware wi-fi using ofdm-based bloom filters

ieeeprojectschennai

attack resilinet mxi zones over road networks architecture and algorithms +91-9994232214,8144199666, ieeeprojectchennai@gmail.com, www.projectsieee.com, www.ieee-projects-chennai.com IEEE PROJECTS 2015-2016 ----------------------------------- Contact:+91-9994232214,+91-8144199666 Email:ieeeprojectchennai@gmail.com Support: ------------- Projects Code Documentation PPT Projects Video File Projects Explanation Teamviewer Support

attack resilinet mxi zones over road networks architecture and algorithms

ieeeprojectschennai

Economic analysis of 4 g upgrade timing

ieeeprojectschennai

Cooperative pseudo bayesian backoff

ieeeprojectschennai

Cooperative load balancing and dynamic channel allocation for cluster based mobile ad hoc networks +91-9994232214,8144199666, ieeeprojectchennai@gmail.com, www.projectsieee.com, www.ieee-projects-chennai.com IEEE PROJECTS 2015-2016 ----------------------------------- Contact:+91-9994232214,+91-8144199666 Email:ieeeprojectchennai@gmail.com Support: ------------- Projects Code Documentation PPT Projects Video File Projects Explanation Teamviewer Support

Cooperative load balancing and dynamic channel allocation for cluster based m...

ieeeprojectschennai

More from ieeeprojectschennai (20)

Parallel application signature for performance analysis and prediction

Dynamic scheduling of irregular stream programs toward many core scalability

Swiper exploiting virtual machine vulnerability in third party clouds with co...

Dynamic scheduling of irregular stream programs toward many core scalability

Swiper exploiting virtual machine vulnerability in third party clouds with co...

Practical conflict graphs in the wild

Design and analysis of medium access protocol throughput and short term fairn...

Fundamental limits of cdf based scheduling throughput, fairness, and feedback...

Ensuring predictable contact opportunity for scalable vehicular internet acce...

Utilize signal traces from others a crowdsourcing perspective of energy savin...

Swimming seamless and efficient wi fi based internet access from moving vehicles

Spatial spectrum access game

On availability performability tradeoff in wireless mesh networks

Managing contention with medley

Impact of location popularity on throughput and delay in mobile ad hoc networks

Frequency diversity aware wi-fi using ofdm-based bloom filters

attack resilinet mxi zones over road networks architecture and algorithms

Economic analysis of 4 g upgrade timing

Cooperative pseudo bayesian backoff

Cooperative load balancing and dynamic channel allocation for cluster based m...

Recently uploaded

On National Teacher Day, meet the 2024-25 Kenan Fellows

Mebane Rash

SKILL OF INTRODUCING THE LESSON MICRO SKILLS.pptx

Amanpreet Kaur

SOC 101 Demonstration of Learning Presentation

camerronhm

Introduction to Nonprofit Accounting: The Basics

TechSoup

psychiatric nursing HISTORY COLLECTION .docx

PoojaSen20

Klinik_ Apotek Onlin 085657271886 Solusi Menggugurkan Masalah Kehamilan Anda Jual Obat Aborsi Asli KLINIK ABORSI TERPEECAYA _ Jual Obat Aborsi Cytotec Misoprostol Asli 100% Ampuh Hanya 3 Jam Langsung Gugur || OBAT PENGGUGUR KANDUNGAN AMPUH MANJUR OBAT ABORSI OLINE" APOTIK Jual Obat Cytotec, Gastrul, Gynecoside Asli Ampuh. JUAL ” Obat Aborsi Tuntas | Obat Aborsi Manjur | Obat Aborsi Ampuh | Obat Penggugur Janin | Obat Pencegah Kehamilan | Obat Pelancar Haid | Obat terlambat Bulan | Ciri Obat Aborsi Asli | Obat Telat Bulan | Pil Aborsi Asli | Cara Menggugurkan Konten | Cara Aborsi Tuntas | Harga Obat Aborsi Asli | Pil Aborsi | Jual Obat Aborsi Cytotec | Cara Aborsi Sendiri | Cara Aborsi Usia 1 Bulan | Cara Aborsi Usia 2 Tahun | Cara Aborsi Usia 3 Bulan | Obat Aborsi Usia 4 Bulan | Cara Abrasi Usia 5 Bulan | Cara Menggugurkan Konten | Kandungan Obat Penggugur | Cara Menghitung Usia Konten | Cara Mengatasi Terlambat Bulan | Penjual Obat Aborsi Asli | Obat Aborsi Garansi | Kandungan Obat Peluntur | Obat Telat Datang Bulan | Obat Telat Haid | Obat Aborsi Paling Murah | Klinik Jual Obat Aborsi | Jual Pil Cytotec | Apotik Jual Obat Aborsi | Kandungan Dokter Abrasi | Cara Aborsi Cepat | Jual Obat Aborsi Bergaransi | Jual Obat Cytotec Asli | Obat Aborsi Aman Manjur | Obat Misoprostol Cytotec Asli. "APA ITU ABORSI" “Aborsi Adalah dengan membendung hormon yang di perlukan untuk mempertahankan kehamilan yaitu hormon progesteron, karena hormon ini dibendung, maka jalur kehamilan mulai membuka dan leher rahim menjadi melunak,sehingga mengeluarkan darah yang merupakan tanda bahwa obat telah bekerja || maksimal 1 jam obat diminum || PENJELASAN OBAT ABORSI USIA 1 _7 BULAN Pada usia kandungan ini, pasien akan merasakan sakit yang sedikit tidak berlebihan || sekitar 1 jam ||. namun hanya akan terjadi pada saatdarah keluar merupakan pertanda menstruasi. Hal ini dikarenakan pada usiakandungan 3 bulan,janin sudah terbentuk sebesar kepalan tangan orang dewasa. Cara kerja obat aborsi : JUAL OBAT ABORSI AMPUH dosis 3 bulan secara umum sama dengan cara kerja || DOSIS OBAT ABORSI 2 bulan”, hanya berbedanya selain mengisolasijanin juga menghancurkan janin dengan formula methotrexate dikandungdidalamnya. Formula methotrexate ini sangat ampuh untuk menghancurkan janinmenjadi serpihan-serpihan kecil akan sangat berguna pada saat dikeluarkan nanti. APA ALASAN WANITA MELAKUKAN ABORSI? Aborsi di lakukan wanita hamil baik yang sudah menikah maupun belum menikah dengan berbagai alasan , akan tetapi alasan yang utama adalah alasan-alasan non medis (termasuk aborsi sendiri / di sengaja/ buatan] MELAYANI PEMESANAN OBAT ABORSI SETIAP HARI, SIAP KIRIM KESELURUH KOTA BESAR DI INDONESIA DAN LUAR NEGERI. HUBUNGI PEMESANAN LEBIH NYAMAN VIA WA/: 085657271886

Jual Obat Aborsi Hongkong ( Asli No.1 ) 085657271886 Obat Penggugur Kandungan...

ZurliaSoop

Application orientated numerical on hev.ppt

RamjanShidvankar

UGC NET Paper 1 Mathematical Reasoning & Aptitude.pdf

Nirmal Dwivedi

microwave assisted reaction. General introduction

Maksud Ahmed

Dyslexia AI Workshop for Slideshare.pptx

callscotland1987

Asian American Pacific Islander Month DDSD 2024.pptx

David Douglas School District

The basics of sentences session 3pptx.pptx

heathfieldcps1

How to Manage Global Discount in Odoo 17 POS

Celine George

Explore the world of IT certification with CompTIA. Discover how the CompTIA Security+ Book SY0-701 can elevate your cybersecurity expertise and open doors to new career opportunities. This PDF provides essential insights into the CompTIA Security+ certification, guiding you through exam preparation and showcasing the benefits of becoming CompTIA-certified. Download now to embark on your journey to IT excellence with CompTIA.

ComPTIA Overview | Comptia Security+ Book SY0-701

bronxfugly43

Mehran University Newsletter Vol-X, Issue-I, 2024

Mehran University of Engineering & Technology, Jamshoro

Seal of Good Local Governance (SGLG) 2024Final.pptx

negromaestrong

General Principles of Intellectual Property: Concepts of Intellectual Proper...

Poonam Aher Patil

Magic bus Group work1and 2 (Team 3).pptx

dhanalakshmis0310

Spellings Wk 3 English CAPS CARES Please Practise

AnaAcapella

Python Notes for mca i year students osmania university.docx

Ramakrishna Reddy Bijjam

Recently uploaded (20)

On National Teacher Day, meet the 2024-25 Kenan Fellows

SKILL OF INTRODUCING THE LESSON MICRO SKILLS.pptx

SOC 101 Demonstration of Learning Presentation

Introduction to Nonprofit Accounting: The Basics

psychiatric nursing HISTORY COLLECTION .docx

Jual Obat Aborsi Hongkong ( Asli No.1 ) 085657271886 Obat Penggugur Kandungan...

Application orientated numerical on hev.ppt

UGC NET Paper 1 Mathematical Reasoning & Aptitude.pdf

microwave assisted reaction. General introduction

Dyslexia AI Workshop for Slideshare.pptx

Asian American Pacific Islander Month DDSD 2024.pptx

The basics of sentences session 3pptx.pptx

How to Manage Global Discount in Odoo 17 POS

ComPTIA Overview | Comptia Security+ Book SY0-701

Mehran University Newsletter Vol-X, Issue-I, 2024

Seal of Good Local Governance (SGLG) 2024Final.pptx

General Principles of Intellectual Property: Concepts of Intellectual Proper...

Magic bus Group work1and 2 (Team 3).pptx

Spellings Wk 3 English CAPS CARES Please Practise

Python Notes for mca i year students osmania university.docx

Parallel application signature for performance analysis and prediction

1. Parallel Application Signature for Performance Analysis and Prediction Abstract: Predicting the performance of parallel scientific applications is becoming increasingly complex. Our goal was to characterize the behavior of message-passing applications on different target machines. To achieve this goal, we developed a method called parallel application signature for performance prediction (PAS2P), which strives to describe an application based on its behavior. Based on the application’s message-passing activity, we identified and extracted representative phases, with which we created a parallel application signature that enabled us to predict the application’s performance. We experimented with using different scientific applications on different clusters. We were able to predict execution times with an average accuracy greater than 97 percent. Hardware Requirements: • System : Pentium IV 2.4 GHz. • Hard Disk ` : 40 GB. • Floppy Drive : 1.44 Mb.

2. • Monitor : 15 VGA Colour. • Mouse : Logitech. • RAM : 256 Mb. Software Requirements: • Operating system : - Windows XP. • Front End : - JSP • Back End : - SQL Server Software Requirements: • Operating system : - Windows XP. • Front End : - .Net • Back End : - SQL Server

Parallel application signature for performance analysis and prediction

Recommended

Recommended

More Related Content

What's hot

What's hot (19)

Viewers also liked

Viewers also liked (13)

Similar to Parallel application signature for performance analysis and prediction

Similar to Parallel application signature for performance analysis and prediction (20)

More from ieeeprojectschennai

More from ieeeprojectschennai (20)

Recently uploaded

Recently uploaded (20)

Parallel application signature for performance analysis and prediction