The VTune analyzer provides an integrated performance analysis and tuning environment that helps you analyze your code's performance on systems with IA-32, Intel(R) 64, and IA-64 architecture.
An Introduction to OpenCL™ Programming with AMD GPUs - AMD & Acceleware WebinarAMD Developer Central
This deck presents highlights from the Introduction to OpenCL™ Programming Webinar presented by Acceleware & AMD on Sept. 17, 2014. Watch a replay of this popular webinar on the AMD Dev Central YouTube channel here: https://www.youtube.com/user/AMDDevCentral or here for the direct link: http://bit.ly/1r3DgfF
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Tom and Spike classifier using TensorFlow Object Detection. Presentation slides of the meetup TFOD conducted on 17/11/2018 at Algoscale Technologies Inc.
This is a presentation I gave as a short overview of LSTMs. The slides are accompanied by two examples which apply LSTMs to Time Series data. Examples were implemented using Keras. See links in slide pack.
Introduction For seq2seq(sequence to sequence) and RNNHye-min Ahn
This is my slides for introducing sequence to sequence model and Recurrent Neural Network(RNN) to my laboratory colleagues.
Hyemin Ahn, @CPSLAB, Seoul National University (SNU)
An Introduction to OpenCL™ Programming with AMD GPUs - AMD & Acceleware WebinarAMD Developer Central
This deck presents highlights from the Introduction to OpenCL™ Programming Webinar presented by Acceleware & AMD on Sept. 17, 2014. Watch a replay of this popular webinar on the AMD Dev Central YouTube channel here: https://www.youtube.com/user/AMDDevCentral or here for the direct link: http://bit.ly/1r3DgfF
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Tom and Spike classifier using TensorFlow Object Detection. Presentation slides of the meetup TFOD conducted on 17/11/2018 at Algoscale Technologies Inc.
This is a presentation I gave as a short overview of LSTMs. The slides are accompanied by two examples which apply LSTMs to Time Series data. Examples were implemented using Keras. See links in slide pack.
Introduction For seq2seq(sequence to sequence) and RNNHye-min Ahn
This is my slides for introducing sequence to sequence model and Recurrent Neural Network(RNN) to my laboratory colleagues.
Hyemin Ahn, @CPSLAB, Seoul National University (SNU)
Comparing Incremental Learning Strategies for Convolutional Neural NetworksVincenzo Lomonaco
In the last decade, Convolutional Neural Networks (CNNs) have shown to perform incredibly well in many computer vision tasks such as object recognition and object detection, being able to extract meaningful high-level invariant features. However, partly because of their complex training and tricky hyper-parameters tuning, CNNs have been scarcely studied in the context of incremental learning where data are available in consecutive batches and retraining the model from scratch is unfeasible. In this work we compare different incremental learning strategies for CNN based architectures, targeting real-word applications.
If you are interested in this work please cite:
Lomonaco, V., & Maltoni, D. (2016, September). Comparing Incremental Learning Strategies for Convolutional Neural Networks. In IAPR Workshop on Artificial Neural Networks in Pattern Recognition (pp. 175-184). Springer International Publishing.
For further information visit my website: http://www.vincenzolomonaco.com/
Deep Learning: Recurrent Neural Network (Chapter 10) Larry Guo
This Material is an in_depth study report of Recurrent Neural Network (RNN)
Material mainly from Deep Learning Book Bible, http://www.deeplearningbook.org/
Topics: Briefing, Theory Proof, Variation, Gated RNNN Intuition. Real World Application
Application (CNN+RNN on SVHN)
Also a video (In Chinese)
https://www.youtube.com/watch?v=p6xzPqRd46w
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Generalized Pipeline Parallelism for DNN TrainingDatabricks
DNN training is extremely time-consuming, necessitating efficient multi-accelerator parallelization. Current approaches to parallelizing training primarily use intra-batch parallelization, where a single iteration of training is split over the available workers, but suffer from diminishing returns at higher worker counts. We present PipeDream, a system that adds inter-batch pipelining to intra-batch parallelism to further improve parallel training throughput, helping to better overlap computation with communication and reduce the amount of communication when possible. Unlike traditional pipelining, DNN training is bi-directional, where a forward pass through the computation graph is followed by a backward pass that uses state and intermediate data computed during the forward pass.
Develop a fundamental overview of Google TensorFlow, one of the most widely adopted technologies for advanced deep learning and neural network applications. Understand the core concepts of artificial intelligence, deep learning and machine learning and the applications of TensorFlow in these areas.
The deck also introduces the Spotle.ai masterclass in Advanced Deep Learning With Tensorflow and Keras.
Comparing Incremental Learning Strategies for Convolutional Neural NetworksVincenzo Lomonaco
In the last decade, Convolutional Neural Networks (CNNs) have shown to perform incredibly well in many computer vision tasks such as object recognition and object detection, being able to extract meaningful high-level invariant features. However, partly because of their complex training and tricky hyper-parameters tuning, CNNs have been scarcely studied in the context of incremental learning where data are available in consecutive batches and retraining the model from scratch is unfeasible. In this work we compare different incremental learning strategies for CNN based architectures, targeting real-word applications.
If you are interested in this work please cite:
Lomonaco, V., & Maltoni, D. (2016, September). Comparing Incremental Learning Strategies for Convolutional Neural Networks. In IAPR Workshop on Artificial Neural Networks in Pattern Recognition (pp. 175-184). Springer International Publishing.
For further information visit my website: http://www.vincenzolomonaco.com/
Deep Learning: Recurrent Neural Network (Chapter 10) Larry Guo
This Material is an in_depth study report of Recurrent Neural Network (RNN)
Material mainly from Deep Learning Book Bible, http://www.deeplearningbook.org/
Topics: Briefing, Theory Proof, Variation, Gated RNNN Intuition. Real World Application
Application (CNN+RNN on SVHN)
Also a video (In Chinese)
https://www.youtube.com/watch?v=p6xzPqRd46w
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Generalized Pipeline Parallelism for DNN TrainingDatabricks
DNN training is extremely time-consuming, necessitating efficient multi-accelerator parallelization. Current approaches to parallelizing training primarily use intra-batch parallelization, where a single iteration of training is split over the available workers, but suffer from diminishing returns at higher worker counts. We present PipeDream, a system that adds inter-batch pipelining to intra-batch parallelism to further improve parallel training throughput, helping to better overlap computation with communication and reduce the amount of communication when possible. Unlike traditional pipelining, DNN training is bi-directional, where a forward pass through the computation graph is followed by a backward pass that uses state and intermediate data computed during the forward pass.
Develop a fundamental overview of Google TensorFlow, one of the most widely adopted technologies for advanced deep learning and neural network applications. Understand the core concepts of artificial intelligence, deep learning and machine learning and the applications of TensorFlow in these areas.
The deck also introduces the Spotle.ai masterclass in Advanced Deep Learning With Tensorflow and Keras.
CyberLab Training Division :
Intel VTune Amplifier is a commercial application for software performance analysis for 32 and 64-bit x86 based machines, and has both GUI and command line interfaces. It is available for both Linux and Microsoft Windows operating systems. Although basic features work on both Intel and AMD hardware, advanced hardware-based sampling requires an Intel-manufactured CPU.
Whether you are tuning for the first time or doing advanced performance optimization, Intel® VTune Amplifier provides a rich set of performance insight into CPU & GPU performance, threading performance & scalability, bandwidth, caching and much more. Analysis is faster and easier because VTune Amplifier understands common threading models and presents information at a higher level that is easier to interpret. Use its powerful analysis to sort, filter and visualize results on the timeline and on your source.
It is available as part of Intel Parallel Studio or as a stand-alone product.
VTune Amplifier assists in various kinds of code profiling including stack sampling, thread profiling and hardware event sampling. The profiler result consists of details such as time spent in each sub routine which can be drilled down to the instruction level. The time taken by the instructions are indicative of any stalls in the pipeline during instruction execution. The tool can be also used to analyze thread performance. The new GUI can filter data based on a selection in the timeline.
For More Details.
Visit: http://www.cyberlabzone.com
PID Control Of Sampled Measurements - Greg McMillan Deminar SeriesJim Cahill
This presentation, PID Control of Sampled Measurements, is from the first in Greg McMillan's live seminar / demo (a.k.a. deminar) series.
You can watch a recorded version of this presentation at: http://www.screencast.com/t/ODhlOWY4M
For future events and background, visit: http://www.emersonprocessxperts.com/archives/2010/04/free_series_of.html
How to bootstrap an SRE team into your company. How to hire them, what to have them work on and how to interact with them as a team. Finally some thought on general practices to consider before your SREs arrive. There are also kitten pictures.
Use of laboratory instruments and specimen processing equipment to perform clinical laboratory assays with only minimal involvement of technologist .
Automation in clinical laboratory is a process by which analytical instruments perform many tests with the least involvement of an analyst.
The International Union of Pure and Applied Chemistry (IUPAC) define automation as "The replacement of human manipulative effort and facilities in the performance of a given process by mechanical and instrumental devices that are regulated by feedback of information so that an apparatus is self-monitoring or self adjusting”.
CyberLab Training Division :
Intel VTune Amplifier is a commercial application for software performance analysis for 32 and 64-bit x86 based machines, and has both GUI and command line interfaces. It is available for both Linux and Microsoft Windows operating systems. Although basic features work on both Intel and AMD hardware, advanced hardware-based sampling requires an Intel-manufactured CPU.
Whether you are tuning for the first time or doing advanced performance optimization, Intel® VTune Amplifier provides a rich set of performance insight into CPU & GPU performance, threading performance & scalability, bandwidth, caching and much more. Analysis is faster and easier because VTune Amplifier understands common threading models and presents information at a higher level that is easier to interpret. Use its powerful analysis to sort, filter and visualize results on the timeline and on your source.
It is available as part of Intel Parallel Studio or as a stand-alone product.
VTune Amplifier assists in various kinds of code profiling including stack sampling, thread profiling and hardware event sampling. The profiler result consists of details such as time spent in each sub routine which can be drilled down to the instruction level. The time taken by the instructions are indicative of any stalls in the pipeline during instruction execution. The tool can be also used to analyze thread performance. The new GUI can filter data based on a selection in the timeline.
For More Details.
Visit: http://www.cyberlabzone.com
CyberLab Training Division :
Intel VTune Amplifier is a commercial application for software performance analysis for 32 and 64-bit x86 based machines, and has both GUI and command line interfaces. It is available for both Linux and Microsoft Windows operating systems. Although basic features work on both Intel and AMD hardware, advanced hardware-based sampling requires an Intel-manufactured CPU.
Whether you are tuning for the first time or doing advanced performance optimization, Intel® VTune Amplifier provides a rich set of performance insight into CPU & GPU performance, threading performance & scalability, bandwidth, caching and much more. Analysis is faster and easier because VTune Amplifier understands common threading models and presents information at a higher level that is easier to interpret. Use its powerful analysis to sort, filter and visualize results on the timeline and on your source.
It is available as part of Intel Parallel Studio or as a stand-alone product.
VTune Amplifier assists in various kinds of code profiling including stack sampling, thread profiling and hardware event sampling. The profiler result consists of details such as time spent in each sub routine which can be drilled down to the instruction level. The time taken by the instructions are indicative of any stalls in the pipeline during instruction execution. The tool can be also used to analyze thread performance. The new GUI can filter data based on a selection in the timeline.
For More Details.
Visit: http://www.cyberlabzone.com
CyberLab Training Division :
Intel VTune Amplifier is a commercial application for software performance analysis for 32 and 64-bit x86 based machines, and has both GUI and command line interfaces. It is available for both Linux and Microsoft Windows operating systems. Although basic features work on both Intel and AMD hardware, advanced hardware-based sampling requires an Intel-manufactured CPU.
Whether you are tuning for the first time or doing advanced performance optimization, Intel® VTune Amplifier provides a rich set of performance insight into CPU & GPU performance, threading performance & scalability, bandwidth, caching and much more. Analysis is faster and easier because VTune Amplifier understands common threading models and presents information at a higher level that is easier to interpret. Use its powerful analysis to sort, filter and visualize results on the timeline and on your source.
It is available as part of Intel Parallel Studio or as a stand-alone product.
VTune Amplifier assists in various kinds of code profiling including stack sampling, thread profiling and hardware event sampling. The profiler result consists of details such as time spent in each sub routine which can be drilled down to the instruction level. The time taken by the instructions are indicative of any stalls in the pipeline during instruction execution. The tool can be also used to analyze thread performance. The new GUI can filter data based on a selection in the timeline.
For More Details.
Visit: http://www.cyberlabzone.com
Software Development Tools for Intel® IoT PlatformsIntel® Software
This talk familiarizes participants with the benefits of using the Intel® software development tools and libraries for developing end-to-end IoT solutions.
E5 Intel Xeon Processor E5 Family Making the Business Case Intel IT Center
This presentation highlights cloud computing advantages of the Intel® Xeon® processor E5 family and helps you make the business case for investing. Includes access to an ROI calculator.
Design and Optimize your code for high-performance with Intel® Advisor and I...Tyrone Systems
For all that we’re unable to attend or would like to recap our live webinar Unleash the Secrets of Performance Profiling with Intel® oneAPI Profiling Tools, all the resources you need are available to you!
Learn about locating and removing bottlenecks is an inherent challenge for every application developer. And it’s made more complex when porting an app to a new platform (say, from a CPU to a GPU). Developers must not only identify bottlenecks; they must figure out which parts of the code will benefit from offloading in the first place. This webinar will focus on how to do just that using two profiling tools from Intel: Intel® VTune Amplifier and Intel Advisor.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Neuro-symbolic is not enough, we need neuro-*semantic*
Intel VTune
1. Intel VTune
Performance Analyzer
Semester – C By - Vikram Singh Saini
Year – 2009
2. Intel VTune –– Performance Analyzer
Intel VTune Performance Analyzer
INTRODUCTION
Intel VTune is a software analysis tool that enables you
to analyze the performance of your application.
Use Sampling to gain accurate representation of your
software’s actual performance.
Code Optimization and Performance
Tuning Using Intel VTune
Produce picture of program flow to identify critical
functions & call sequences using Call Graph profiling.
Track system activity and resource consumption during
runtime with Counter Monitor.
Tune code more efficiently using Tuning Assistant.
2
3. Intel VTune –– Performance Analyzer
Intel VTune Performance Analyzer
SAMPLING
Sampling is the process of collecting set of data for
analysis & representing analyzed data in statistical format.
Sampling helps you to identify:-
1. Hotspots – Section of code within module that takes
Code Optimization and Performance
long time to execute.
2. Bottlenecks Using Intel VTune that slows down the
Tuning – Area in the code
execution of the application.
Two types of sampling mechanism:
1. Time-Based sampling.
2. Event –Based sampling.
3
4. Intel VTune –– Performance Analyzer
Intel VTune Performance Analyzer
SAMPLING (Contd…)
TIME – BASED SAMPLING
Collects samples of activity at regular intervals.
Time based sampling uses the OS timer to calculate
Code Optimization and Performance
the time Tuning Using Intel VTune
interval for collecting samples.
Collected samples displays the performance data of all
the processes running on computer.
The process that takes the longest time to execute
contains the largest number of samples.
4
5. Intel VTune –– Performance Analyzer
Intel VTune Performance Analyzer
SAMPLING (Contd…)
EVENT – BASED SAMPLING
Code Optimization and Performance
Tuning Using Intel VTune
5
6. Intel VTune –– Performance Analyzer
Intel VTune Performance Analyzer
SAMPLING (Contd…)
EVENT – BASED SAMPLING
Event based sampling is performed on basis of
processor events.
Code Optimization and Performance
By using EBS, Using Intel VTune which
Tuning one can determine
process,thread,module,function or code line in the
application is generating the largest number of processor
events .
Using EBS you can view the corresponding events
which are taking part while application is executing.
6
7. Intel VTune –– Performance Analyzer
Intel VTune Performance Analyzer
SAMPLING (Contd…)
SAMPLING OVER TIME
Code Optimization and Performance
Tuning Using Intel VTune
7
8. Intel VTune –– Performance Analyzer
Intel VTune Performance Analyzer
SAMPLING (Contd…)
SAMPLING OVER TIME
Sampling OverTime view displays the samples
collected with respect to time for a single event.
Code Optimization and Performance
Enables you to identify which threads are running
Tuning Using Intel VTune
serially & in parallel at any point in time.
Can gather following information:-
@ Context switching
@ Processor utilization
@ Thread interaction
@ Temporal location of Hotspots
8
9. Intel VTune –– Performance Analyzer
Intel VTune Performance Analyzer
CALL GRAPHS
It helps you to obtain information about the functional
flow of an application.
One can identify the critical path of the application or
module.
Code Optimization and Performance
Tuning Using Intel VTune
Identify function which took long time and can be
optimized.
Intel Vtune displays the results of the call graph in
three views:
I. Graph view.
II. Call list view.
III. Function summary view.
9
11. Intel VTune –– Performance Analyzer
Intel VTune Performance Analyzer
CALL GRAPHS (Contd…)
CALL LIST VIEW
Code Optimization and Performance
Tuning Using Intel VTune
11
12. Intel VTune –– Performance Analyzer
Intel VTune Performance Analyzer
CALL GRAPHS (Contd…)
FUNCTION SUMMARY VIEW
Code Optimization and Performance
Tuning Using Intel VTune
12
13. Intel VTune –– Performance Analyzer
Intel VTune Performance Analyzer
COUNTER MONITOR
Counter Monitor identifies system level issues in
application when the application runs on the system.
It is used to track system activity and resource
consumption during runtime.
Code Optimization and Performance
Tuning Using Intel VTune
Measures and gathers performance -related data that
represents the state of the system.
Three views of analyzing the result:-
# Runtime data view
# Logged data view
# Summary data view
13
14. Intel VTune –– Performance Analyzer
Intel VTune Performance Analyzer
COUNTER MONITOR (Contd…)
LOGGED DATA VIEW
Code Optimization and Performance
Tuning Using Intel VTune
14
17. Intel VTune –– Performance Analyzer
Intel VTune Performance Analyzer
Tuning Assistant
Observe the performance issues of your application
and provides advice in the form of a tuning advice report.
Application can be tuned at three levels:
@ System-Level
Code Optimization and Performance
@ Application-Level
Tuning Using Intel VTune
@ Microarchitecture - Level
Three strategies to improve performance of app.:
- Balancing I/O computation
- Improving threading model
- Improving efficiency of computation
17