The document discusses various performance profiling methods including sampling, instrumentation, concurrency profiling, .NET memory profiling, and tier interaction profiling. It describes the values and views provided by each method. Sampling collects statistical data and is lightweight, while instrumentation collects detailed timing information for function calls. Concurrency profiling collects information about threads and blocking events. .NET memory profiling interrupts on object allocations and garbage collection to collect allocation data. The general profiler views include summary, call tree, modules, caller/callee, functions, .NET memory, object lifetime, and thread details views. Examples demonstrate using the views to identify performance issues and improvements.
Quick overview on Visual Studio 2012 Profiler & Profiling tools : the importance of the profiling methods (sampling, instrumentation, memory, concurrency, … ), how to run a profiling session, how to profile unit test/load test, how to use API and a few samples
Spark Autotuning: Spark Summit East talk by Lawrence SpracklenSpark Summit
While the performance delivered by Spark has enabled data scientists to undertake sophisticated analyses on big and complex data in actionable timeframes, too often, the process of manually configuring the underlying Spark jobs (including the number and size of the executors) can be a significant and time consuming undertaking. Not only it does this configuration process typically rely heavily on repeated trial-and-error, it necessitates that data scientists have a low-level understanding of Spark and detailed cluster sizing information. At Alpine Data we have been working to eliminate this requirement, and develop algorithms that can be used to automatically tune Spark jobs with minimal user involvement,
In this presentation, we discuss the algorithms we have developed and illustrate how they leverage information about the size of the data being analyzed, the analytical operations being used in the flow, the cluster size, configuration and real-time utilization, to automatically determine the optimal Spark job configuration for peak performance.
Quick overview on Visual Studio 2012 Profiler & Profiling tools : the importance of the profiling methods (sampling, instrumentation, memory, concurrency, … ), how to run a profiling session, how to profile unit test/load test, how to use API and a few samples
Spark Autotuning: Spark Summit East talk by Lawrence SpracklenSpark Summit
While the performance delivered by Spark has enabled data scientists to undertake sophisticated analyses on big and complex data in actionable timeframes, too often, the process of manually configuring the underlying Spark jobs (including the number and size of the executors) can be a significant and time consuming undertaking. Not only it does this configuration process typically rely heavily on repeated trial-and-error, it necessitates that data scientists have a low-level understanding of Spark and detailed cluster sizing information. At Alpine Data we have been working to eliminate this requirement, and develop algorithms that can be used to automatically tune Spark jobs with minimal user involvement,
In this presentation, we discuss the algorithms we have developed and illustrate how they leverage information about the size of the data being analyzed, the analytical operations being used in the flow, the cluster size, configuration and real-time utilization, to automatically determine the optimal Spark job configuration for peak performance.
Monitorama 2015 talk by Brendan Gregg, Netflix. With our large and ever-changing cloud environment, it can be vital to debug instance-level performance quickly. There are many instance monitoring solutions, but few come close to meeting our requirements, so we've been building our own and open sourcing them. In this talk, I will discuss our real-world requirements for instance-level analysis and monitoring: not just the metrics and features we desire, but the methodologies we'd like to apply. I will also cover the new and novel solutions we have been developing ourselves to meet these needs and desires, which include use of advanced Linux performance technologies (eg, ftrace, perf_events), and on-demand self-service analysis (Vector).
(ATS6-APP01) Unleashing the Power of Your Data with DiscoverantBIOVIA
In the fast-paced, high demand environment of manufacturing, it’s almost impossible to find the time to gather large amounts of data and organize it into a common context. This session presents best practices for using Discoverant hierarchies and Direct Connects to provide your end-users with on-demand and scheduled self-service access to their contextualized data, freeing their time for the more important trending and analysis activities that enable improved process performance and predictability.
Webinar: Zing Vision: Answering your toughest production Java performance que...Azul Systems Inc.
Solving Java performance issues in production can be frustrating. You’re left in the dark about what could be causing the problems because standard Java tools have too much performance overhead for production use. They’re designed for development or pre-production testing and realistically can’t be used to monitor a business-critical application during peak loads, which is when the problems occur!
Zing Vision is your flashlight. Its low overhead metric collection is built into Zing, Azul’s high performance virtual machine, and designed to run in production with zero performance overhead. At last, you can see your applications’ operation at the thread level, track memory usage, find “hot” code and even save data for later analysis. In this webinar, Joseph Coha, Azul Systems Senior Staff Engineer, describes how Zing Vision works, shows sample data and discusses how you can use this information to find and fix your most stubborn production performance issues. He also tells you how you can download and try Zing and Zing Vision with your current applications to see for yourself how far you can take the performance of your existing apps.
Speech of Dmytro Shapovalov, Infrastructure Engineer at Cossack Labs, at Ruby Meditation #26 Kyiv 16.02.2019
Next conference - http://www.rubymeditation.com/
Most modern applications live in a close cooperation with each other. We will talk about the ways to effectively use the modern techniques for monitoring the health of applications and look on tasks and typical implementation mistakes through the eyes of an infrastructure engineer. And we will also consider the Ruby libraries that help to implement all of this.
Announcements and conference materials https://www.fb.me/RubyMeditation
News https://twitter.com/RubyMeditation
Photos https://www.instagram.com/RubyMeditation
The stream of Ruby conferences (not just ours) https://t.me/RubyMeditation
Siddhi: A Second Look at Complex Event Processing ImplementationsSrinath Perera
Today there are so much data being available from sources like sensors (RFIDs, Near Field Communication), web activities, transactions, social networks, etc. Making sense of this avalanche of data requires efficient and fast processing.
Processing of high volume of events to derive higher-level information is a vital part of taking critical decisions, and
Complex Event Processing (CEP) has become one of the most rapidly emerging fields in data processing. e-Science
use-cases, business applications, financial trading applications, operational analytics applications and business activity monitoring applications are some use-cases that directly use CEP. This paper discusses different design decisions associated
with CEP Engines, and proposes some approaches to improve CEP performance by using more stream processing
style pipelines. Furthermore, the paper will discuss Siddhi, a CEP Engine that implements those suggestions. We
present a performance study that exhibits that the resulting CEP Engine—Siddhi—has significantly improved performance.
Primary contributions of this paper are performing a critical analysis of the CEP Engine design and identifying
suggestions for improvements, implementing those improvements
through Siddhi, and demonstrating the soundness of those suggestions through empirical evidence.
Application of the Actor Model to Large Scale NDE Data AnalysisChrisCoughlin9
The Actor model of concurrent computation discretizes a problem into a series of independent units or actors that interact only through the exchange of messages. Without direct coupling between individual components, an Actor-based system is inherently concurrent and fault-tolerant. These traits lend themselves to so-called “Big Data” applications in which the volume of data to analyze requires a distributed multi-system design. For a practical demonstration of the Actor computational model, a system was developed to assist with the automated analysis of Nondestructive Evaluation (NDE) datasets using the open source Myriad Data Reduction Framework. A machine learning model trained to detect damage in two-dimensional slices of C-Scan data was deployed in a streaming data processing pipeline. To demonstrate the flexibility of the Actor model, the pipeline was deployed on a local system and re-deployed as a distributed system without recompiling, reconfiguring, or restarting the running application.
Apache Spark Performance Troubleshooting at Scale, Challenges, Tools, and Met...Databricks
This talk is about methods and tools for troubleshooting Spark workloads at scale and is aimed at developers, administrators and performance practitioners. You will find examples illustrating the importance of using the right tools and right methodologies for measuring and understanding performance, in particular highlighting the importance of using data and root cause analysis to understand and improve the performance of Spark applications. The talk has a strong focus on practical examples and on tools for collecting data relevant for performance analysis. This includes tools for collecting Spark metrics and tools for collecting OS metrics. Among others, the talk will cover sparkMeasure, a tool developed by the author to collect Spark task metric and SQL metrics data, tools for analysing I/O and network workloads, tools for analysing CPU usage and memory bandwidth, tools for profiling CPU usage and for Flame Graph visualization.
AADL: Architecture Analysis and Design LanguageIvano Malavolta
This presentation is about a lecture I gave within the "Software systems and services" immigration course at the Gran Sasso Science Institute, L'Aquila (Italy): http://cs.gssi.infn.it/.
http://www.ivanomalavolta.com
A machine learning and data science pipeline for real companiesDataWorks Summit
Comcast is one of the largest cable and telecommunications providers in the country built on decades of mergers, acquisitions, and subscriber growth. The success of our company depends on keeping our customers happy and how quickly we can pivot with changing trends and new technologies. Data abounds within our internal data centers and edge networks as well as both the private and public cloud across multiple vendors.
Within such an environment and given such challenges, how do we get AI, machine learning, and data science platforms built so our company can respond to the market, predict our customers’ needs and create new revenue generating products that delight our customers? If you don’t happen to be our friends and colleagues at Google, Facebook, and Amazon, what are technologies, strategies, and toolkits you can employ to bring together disparate data sets and quickly get them into the hands of your data scientists and then into your own production systems for use by your customers and business partners?
We’ll explore our journey and evolution and look at specific technologies and decisions that have gotten us to where we are today and demo how our platform works.
Speaker
Ray Harrison, Comcast, Enterprise Architect
Prashant Khanolkar, Comcast, Principal Architect Big Data
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
Monitorama 2015 talk by Brendan Gregg, Netflix. With our large and ever-changing cloud environment, it can be vital to debug instance-level performance quickly. There are many instance monitoring solutions, but few come close to meeting our requirements, so we've been building our own and open sourcing them. In this talk, I will discuss our real-world requirements for instance-level analysis and monitoring: not just the metrics and features we desire, but the methodologies we'd like to apply. I will also cover the new and novel solutions we have been developing ourselves to meet these needs and desires, which include use of advanced Linux performance technologies (eg, ftrace, perf_events), and on-demand self-service analysis (Vector).
(ATS6-APP01) Unleashing the Power of Your Data with DiscoverantBIOVIA
In the fast-paced, high demand environment of manufacturing, it’s almost impossible to find the time to gather large amounts of data and organize it into a common context. This session presents best practices for using Discoverant hierarchies and Direct Connects to provide your end-users with on-demand and scheduled self-service access to their contextualized data, freeing their time for the more important trending and analysis activities that enable improved process performance and predictability.
Webinar: Zing Vision: Answering your toughest production Java performance que...Azul Systems Inc.
Solving Java performance issues in production can be frustrating. You’re left in the dark about what could be causing the problems because standard Java tools have too much performance overhead for production use. They’re designed for development or pre-production testing and realistically can’t be used to monitor a business-critical application during peak loads, which is when the problems occur!
Zing Vision is your flashlight. Its low overhead metric collection is built into Zing, Azul’s high performance virtual machine, and designed to run in production with zero performance overhead. At last, you can see your applications’ operation at the thread level, track memory usage, find “hot” code and even save data for later analysis. In this webinar, Joseph Coha, Azul Systems Senior Staff Engineer, describes how Zing Vision works, shows sample data and discusses how you can use this information to find and fix your most stubborn production performance issues. He also tells you how you can download and try Zing and Zing Vision with your current applications to see for yourself how far you can take the performance of your existing apps.
Speech of Dmytro Shapovalov, Infrastructure Engineer at Cossack Labs, at Ruby Meditation #26 Kyiv 16.02.2019
Next conference - http://www.rubymeditation.com/
Most modern applications live in a close cooperation with each other. We will talk about the ways to effectively use the modern techniques for monitoring the health of applications and look on tasks and typical implementation mistakes through the eyes of an infrastructure engineer. And we will also consider the Ruby libraries that help to implement all of this.
Announcements and conference materials https://www.fb.me/RubyMeditation
News https://twitter.com/RubyMeditation
Photos https://www.instagram.com/RubyMeditation
The stream of Ruby conferences (not just ours) https://t.me/RubyMeditation
Siddhi: A Second Look at Complex Event Processing ImplementationsSrinath Perera
Today there are so much data being available from sources like sensors (RFIDs, Near Field Communication), web activities, transactions, social networks, etc. Making sense of this avalanche of data requires efficient and fast processing.
Processing of high volume of events to derive higher-level information is a vital part of taking critical decisions, and
Complex Event Processing (CEP) has become one of the most rapidly emerging fields in data processing. e-Science
use-cases, business applications, financial trading applications, operational analytics applications and business activity monitoring applications are some use-cases that directly use CEP. This paper discusses different design decisions associated
with CEP Engines, and proposes some approaches to improve CEP performance by using more stream processing
style pipelines. Furthermore, the paper will discuss Siddhi, a CEP Engine that implements those suggestions. We
present a performance study that exhibits that the resulting CEP Engine—Siddhi—has significantly improved performance.
Primary contributions of this paper are performing a critical analysis of the CEP Engine design and identifying
suggestions for improvements, implementing those improvements
through Siddhi, and demonstrating the soundness of those suggestions through empirical evidence.
Application of the Actor Model to Large Scale NDE Data AnalysisChrisCoughlin9
The Actor model of concurrent computation discretizes a problem into a series of independent units or actors that interact only through the exchange of messages. Without direct coupling between individual components, an Actor-based system is inherently concurrent and fault-tolerant. These traits lend themselves to so-called “Big Data” applications in which the volume of data to analyze requires a distributed multi-system design. For a practical demonstration of the Actor computational model, a system was developed to assist with the automated analysis of Nondestructive Evaluation (NDE) datasets using the open source Myriad Data Reduction Framework. A machine learning model trained to detect damage in two-dimensional slices of C-Scan data was deployed in a streaming data processing pipeline. To demonstrate the flexibility of the Actor model, the pipeline was deployed on a local system and re-deployed as a distributed system without recompiling, reconfiguring, or restarting the running application.
Apache Spark Performance Troubleshooting at Scale, Challenges, Tools, and Met...Databricks
This talk is about methods and tools for troubleshooting Spark workloads at scale and is aimed at developers, administrators and performance practitioners. You will find examples illustrating the importance of using the right tools and right methodologies for measuring and understanding performance, in particular highlighting the importance of using data and root cause analysis to understand and improve the performance of Spark applications. The talk has a strong focus on practical examples and on tools for collecting data relevant for performance analysis. This includes tools for collecting Spark metrics and tools for collecting OS metrics. Among others, the talk will cover sparkMeasure, a tool developed by the author to collect Spark task metric and SQL metrics data, tools for analysing I/O and network workloads, tools for analysing CPU usage and memory bandwidth, tools for profiling CPU usage and for Flame Graph visualization.
AADL: Architecture Analysis and Design LanguageIvano Malavolta
This presentation is about a lecture I gave within the "Software systems and services" immigration course at the Gran Sasso Science Institute, L'Aquila (Italy): http://cs.gssi.infn.it/.
http://www.ivanomalavolta.com
A machine learning and data science pipeline for real companiesDataWorks Summit
Comcast is one of the largest cable and telecommunications providers in the country built on decades of mergers, acquisitions, and subscriber growth. The success of our company depends on keeping our customers happy and how quickly we can pivot with changing trends and new technologies. Data abounds within our internal data centers and edge networks as well as both the private and public cloud across multiple vendors.
Within such an environment and given such challenges, how do we get AI, machine learning, and data science platforms built so our company can respond to the market, predict our customers’ needs and create new revenue generating products that delight our customers? If you don’t happen to be our friends and colleagues at Google, Facebook, and Amazon, what are technologies, strategies, and toolkits you can employ to bring together disparate data sets and quickly get them into the hands of your data scientists and then into your own production systems for use by your customers and business partners?
We’ll explore our journey and evolution and look at specific technologies and decisions that have gotten us to where we are today and demo how our platform works.
Speaker
Ray Harrison, Comcast, Enterprise Architect
Prashant Khanolkar, Comcast, Principal Architect Big Data
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
2. Profiling Methods
• Sampling - collects statistical data about the work
performed by an application.
• Instrumentation - collects detailed timing information
about each function call.
• Concurrency - collects detailed information about
multi-threaded applications.
• .NET memory - collects detailed information about
.NET memory allocation and garbage collection.
• Tier interaction - collects information about
synchronous ADO.NET function calls to a SqlServer
database.
3. Sampling method
• Lightweight
• Little effect on an app
• Initial exploration of the performance an app
• Investigation issues that involve using CPU
4. Sampling method values
• Inclusive samples - the total number of samples that
are collected during the execution of the target
function. (include child functions)
• Exclusive samples - the number of samples that are
collected during the direct execution of the instructions
of the target function. (exclude child function)
• Inclusive percent - the percentage of the total number
of inclusive samples in the profiling run that are
inclusive samples of the function or data range.
• Exclusive percent - the percentage of the total number
of exclusive samples in the profiling run that are
exclusive samples of the function or data range.
5. Instrumentation method
• Detailed timing for the function calls
• Investigating input/output bottlenecks such as
disk I/O.
• Close examination of a particular module or
set of functions.
6. Instrumentation method values
• Elapsed Inclusive - the total time that is spent executing the
function or source line.
• Application Inclusive - the time that is spent executing the function
or source line, but excluding time that is spent in calls to the
operating system.
• Elapsed Exclusive - the time that is spent executing code in the
body of the function or source code line. Time that is spent
executing functions that are called by the function or source line is
excluded.
• Application Exclusive - the time that is spent executing code in the
body of the function or source code line. Time that is spent
executing calls to the operating system and time that is spent
executing functions that are called by the function or source line is
excluded.
7. Concurrency method
• Collects detailed call stack information every
time that competing threads are forced to
wait for access to a shared resource.
• The concurrency visualizer displays graphical
information that you can use to locate
performance bottlenecks, CPU
underutilization, thread contention, thread
migration, synchronization delays, areas of
overlapped I/O, and other information.
8. .NET Memory method
• Interrupts the computer processor at each
allocation of a .NET Framework object
• Also after garbage collection
• Collects object lifetime data
• Collects a type, size, and number of objects
that were created in an allocation or were
destroyed in a garbage collection.
9. General Profiler Data Views
• Summary View – list the functions that were executing most frequently when
samples were collected and the functions that were performing the most
individual work.
• Call Tree View - displays the execution paths of functions in a hierarchical tree.
• Modules View - organizes profiling data by module, and lists the functions, source
code lines, and instructions that were executing when samples were collected.
• Caller / Callee View - displays profiling data for a selected function and the
functions that called and were called by the selected function.
• Functions View - organizes profiling by function, and lists the functions that were
executing when samples were collected.
• .NET Memory Allocations View - lists the types that were allocated in the profiling
run, and the call trees (execution paths) that resulted in the allocation of the type.
• Object Lifetime View - lists the types that were allocated in the profiling run, and
the number of instances, size in bytes, and the garbage collection generation of
the type.
• Thread Details View - displays a graphical timeline of the blocking events for each
thread and lists the call stack for the blocking events.