Intel® Trace Analyzer and Collector

Understand MPI application behavior, quickly finding bottlenecks, and achieving high performance for parallel cluster applications

  • Powerful MPI Communications Profiling and Analysis
  • Scalable - Low Overhead & Effective Visualization
  • Flexible to Fit Workflow – Compile, Link or Run

Intel® Trace Analyzer and Collector 9.0 is a graphical tool for understanding MPI application behavior, quickly finding bottlenecks, improving correctness, and achieving high performance for parallel cluster applications based on Intel architecture. Improve weak and strong scaling for small and large applications with Intel Trace Analyzer and Collector.

Benefits:

  • Visualize and understand parallel application behavior
  • Evaluate profiling statistics and load balancing
  • Analyze performance of subroutines or code blocks
  • Learn about communication patterns, parameters, and performance data
  • Identify communication hotspots
  • Decrease time to solution and increase application efficiency

MPI checking

  • A unique MPI Correctness Checker detects deadlocks, data corruption, and errors with MPI parameters, data types, buffers, communicators, point-to-point messages and collective operations.
  • The Correctness Checker allows the user to scale to extremely large systems and detect errors even among a large number of processes.

Interface and Displays

  • Intel® Trace Analyzer and Collector includes full-color customizable GUI with many drill-down view options.
  • The analyzer is able to extremely rapidly unwind the call stack and use debug information to map instruction addresses to source code.
  • With both command-line and GUI interfaces, the user can additionally set up batch runs or do interactive debugging.

Scalability

  • Low overhead allows random access to portions of a trace, making it suitable for analyzing large amounts of performance data.
  • Thread safety allows you to trace multithreaded MPI applications for event-based tracing as well as non-MPI threaded applications.

Instrumentation and Tracing

  • Low-intrusion instrumentation supports MPI applications with C, C++, or Fortran.
  • Intel Trace Analyzer and Collector automatically records performance data from parallel threads in C, C++, or Fortran

What’s new

  • MPI Communications Profile Summary Overview
    • Quickly Understand Computation vs Communications
    • Identify which MPI communications are being most used
    • Advice of where to start your analysis

  • Expanded Standards Support with MPI 3.0
    • Automated MPI Communications Analysis with Performance Assistant
    • Detect common MPI performance issues
    • Automated tips on potential solutions

Videos to help you get started.

Register for future Webinars


Previously recorded Webinars:

  • Increase Cluster MPI Application Performance with a "MPI Tune" Up
  • MPI on Intel® Xeon Phi™ coprocessor
  • Quickly discover performance issues with the Intel® Trace Analyzer and Collector 9.0 Beta

More Tech Articles

Intel® Trace Analyzer and Collector Structured Tracefile Format
Par Gergana Slavova (Intel)Publié le 04/14/20150
The Intel® Trace Analyzer and Collector employs the use of the Structured Trace File Format (STF) in order to profile MPI applications in a scalable and efficient manner. The development of STF was motivated by the observation that the conventional approach of handling trace data in a single tra...
Using Intel® MPI Library 5.0 with MPICH based applications
Par Dmitry Sivkov (Intel)Publié le 08/25/20140
Why it is needed? Different MPI implementations have their specific benefits and advantages. So in the specific cluster environment the HPC application with the other MPI implementation can probably perform better.  Intel® MPI Library has the following benefits: Support of the wide range of cl...
Intel® Cluster Tools Open Source Downloads
Par Gergana Slavova (Intel)Publié le 03/06/20140
This article makes available third-party libraries and sources that were used in the creation of Intel® Software Development Products. Intel provides this software pursuant to their applicable licenses. Products and Versions: Intel® Trace Analyzer and Collector for Linux* gcc-3.2.3-42.zip (whi...
Using the Intel® MPI Library on Intel® Xeon Phi™ Coprocessor Systems
Par loc-nguyen (Intel)Publié le 03/19/201316
Download Article Download Using the Intel® MPI Library on Intel® Xeon Phi™ Coprocessor Systems [PDF 499KB] Table of Contents Chapter 1 – Introduction 1.1 – Overview 1.2 – Compatibility Chapter 2 – Installing the Intel® MPI Library 2.1 – Installing the Intel MPI Library 2.2 – Preparation Chapter 3...
S’abonner à Articles de la Zone des développeurs Intel

Supplemental Documentation

Intel® Parallel Studio XE 2015 Update 2 Cluster Edition Readme
Par Gergana Slavova (Intel)Publié le 02/06/20150
The Intel® Parallel Studio XE 2015 Update 2 Cluster Edition for Linux* and Windows* combines all Intel® Parallel Studio XE and Intel® Cluster Tools into a single package. This multi-component software toolkit contains the core libraries and tools to efficiently develop, optimize, run, and distrib...
Intel® Parallel Studio XE 2015 Update 1 Cluster Edition Readme
Par Gergana Slavova (Intel)Publié le 11/24/20140
The Intel® Parallel Studio XE 2015 Update 1 Cluster Edition for Linux* and Windows* combines all Intel® Parallel Studio XE and Intel® Cluster Tools into a single package. This multi-component software toolkit contains the core libraries and tools to efficiently develop, optimize, run, and distrib...
Intel® Parallel Studio XE 2015 Cluster Edition Initial Release Readme
Par Gergana Slavova (Intel)Publié le 08/15/20140
The Intel® Parallel Studio XE 2015 Cluster Edition for Linux* and Windows* combines all Intel® Parallel Studio XE and Intel® Cluster Tools into a single package. This multi-component software toolkit contains the core libraries and tools to efficiently develop, optimize, run, and distribute paral...
Intel® Trace Analyzer and Collector 8.1 Update 3 Readme
Par Gergana Slavova (Intel)Publié le 08/13/20130
The Intel® Trace Analyzer and Collector 8.1 Update 3 for Linux* and Windows* is a low-overhead scalable event-tracing library with graphical analysis that reduces the time it takes an application developer to enable maximum performance of cluster applications. This package is for users who dev...
S’abonner à Articles de la Zone des développeurs Intel

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Intel® Parallel Studio XE 2016 Beta program has started!
Par Gergana Slavova (Intel)0
The Intel® Parallel Studio XE 2016 Beta program is now available! In this beta test, you will have early access to Intel® Parallel Studio XE 2016 products and the opportunity to provide feedback to help make our products better. Registration is easy through the pre-Beta survey site. This suite of products brings together exciting new technologies along with improvements to Intel’s existing software development tools: Expanded Standards and Features – Scaling Development Efforts Forward Additional language support for C11 and C++14, Fortran 2008 Submodules and IMPURE ELEMENTAL, and C Interoperability from Fortran 2015, and OpenMP* 4.1 TR 3.  New support for SIMD operator use with SSE integer types, Intel® Cilk™ Plus combined Parallel and SIMD loops, OpenMP* 4.0 user-defined reductions (C++ only), enhanced uninitialized variable detection (Fortran only), feature improvements to Intel’s Language Extensions for Offload, annotated source listings, and a new directory structure.  All a...
qib affinity
Par Brice G.4
Hello We have some dual-Xeon E5v3 hosts (running RHEL6) with a TrueScale HCA connected to a PCI slot close to socket #1. When running Intel MPI Benchmarks, I see better latency and bandwidth when binding to socket #0 (1.27us against 1.42us for socket #1), which looks wrong. Given that a significant part of the TrueScale network processing is done in software, I wonder if this could be caused by the locality of some software tasks/buffers. I see that qib0 interrupts go socket #0, but changing them to go to socket #1 doesn't seem to fix this strangeness. Is there something else to migrate? Any kernel module parameter to move drivers' buffers near socket #1 where they should be? thanks Brice
open_hca: getaddr_netdev ERROR: Connection refused. Is ib0 configured ERROR
Par ELIO M.0
Dear all, I am a beginner in linux; i am using quantum espresso software; The cluster we have at University has three types of partition short (for jobs within an hour), long (jobs within 4 days) and superlong (jobs within 10 days). each node has 8 processors; however recently when I am running a job, only the SHORT partition works properly; this is not very useful for me as I need to run longer jobs. when i run the other two (long and superlong) I get several errors: running on more than one node say : 16 processors (2 nodes) producesan error: "veredas60:30606:  open_hca: getaddr_netdev ERROR: Connection refused. Is ib0 configured? veredas60:30606:  open_hca: getaddr_netdev ERROR: Connection refused. Is ib1 configured? rank 0 in job 1  veredas60_36331   caused collective abort of all ranks   exit status of rank 0: killed by signal 9"   and sometimes:  rank 0 in job 1  veredas14_39459   caused collective abort of all ranks   exit status of rank 0: return code 1   running on one ...
OpenMP problem with Threads Number
Par Rodrigo Antonio F.4
Hi, My program calls a function and it receives two files name that are build in for command. Its goal is read a number of mol2 files and create pdbqt files. Therefore, I create a loop in which each file mol2 file name is created pdbqt file. My part of the code is below: #pragma omp parallel shared(m,mol2_size) private(path_file_mol2, path_file_pdbqt, file_pdbqt, base_file_name) { path_file_mol2 = (char*)malloc(sizeof(char)*MAX_PATH_FILE_NAME); path_file_pdbqt = (char*)malloc(sizeof(char)*MAX_PATH_FILE_NAME); file_pdbqt = (char*)malloc(sizeof(char)*MAX_FILE_NAME); base_file_name = (char*)malloc(sizeof(char)*MAX_FILE_NAME); #pragma omp for for (m = 0; m < mol2_size; m++){ //Obtaing base file name. This is file name without its extension set_base_file_name(base_file_name, all_mol2_files[m].file_name, ex_mol2); strcpy(file_pdbqt, base_file_name); add_ext_file_name(file_pdbqt, ex_pdbqt); //mol2 file strcpy(path_file_mol2, para...
Intel MPI Benchmarks Archives
Par Vaibhav R.0
I wish to use Intel MPI benchmarks for performance analysis of my mpich2 implementation. However, I'm using mpich2-1.4.1 version in my cluster. Where can I download the appropriate benchmarks for this version of mpich2? The latest version is not compatible with the mpich2 version I use. Please help.
Multi-rail ofa fabric not available (Intel MPI Library Version 5.0 Update 3)
Par René O.1
Hello, there is a problem with the latest MPI library and multi-rail support over OFA:
Intel® Parallel Studio XE Cluster Edition for Linux
Par Badlishah J.1
Greetings,​   I am planning to develop some code for my client, to be executed on a 3 node cluster to start. Would I require this software to be installed in each of the nodes?   My client is in the progress to procure licences for this software. However this may take a while. Will it be possible to extend the evaluation period a while longer?   Thank you.   Regards, Badlishah
where is libmpifort.so?
Par dingjun.chencmgl.ca1
HI, Everyone, Today, when I run my hybrid MPI/OPenMP application with MPIRUN on our 3-nodes Infinband Linux-PCs Cluster, the following error occurred: /cmg/dingjun/imexLocal/imex_xsamg_dave.exe: error while loading shared libraries: libmpifort.so.12: cannot open shared object file: No such file or directory Could you tell me how to fix it? I look forward to hearing from you. Thanks in advance.  
S’abonner à Forums
  • What are some key things I can learn about my program using Intel® Trace Analyzer and Collector?
  • The Intel Trace Analyzer and Collector is a graphical tool used primarily for MPI-based programs. It helps you understand your application's behavior across its full runtime. It can help find temporal dependencies in your code and communication bottlenecks across the MPI ranks. It also checks the correctness of your application and points you to potential programming errors, buffer overlaps, and deadlocks.

  • Will Intel Trace Analyzer and Collector only work with Intel MPI Library?
  • No, the Intel Trace Analyzer and Collector support all major MPICH2-based implementations. If you're wondering whether your MPI library can be profiled using the Intel Trace Analyzer and Collector, you can run a simple ABI-compatibility check by compiling the provided mpiconstants.c file and verifying the values with the ones provided in the Intel Trace Collector Reference Guide..

  • Can Intel Trace Analyzer and Collector be used on applications for Intel® Many Integrated Core Architecture (Intel® MIC Architecture)?
  • Yes, Intel MIC Architecture is fully supported by the Intel Trace Analyzer and Collector.

  • What file and directory permissions are required to use Intel Trace Analyzer and Collector?
  • You do not need to install special drivers, kernels, or acquire extra permissions. Simply install the Intel Trace Analyzer and Collector in the $HOME directory and link it with your application of choice from there.

  • Should I recompile/relink my application to collect information?
  • It depends on your application. For Windows* OS, you have to relink your application by using the –trace link-time flag.

    For Linux* OS (and if your application is dynamically linked), you do not need to relink or recompile. Simply use the –trace option at runtime (for example: mpirun –trace).

  • How do I control which part of my application should be profiled?
  • The Intel Trace Collector provides several options to control the data collection. By default, only information about MPI calls is collected. If you'd like to filter which MPI calls should be traced, create a configuration file and set the VT_CONFIG environment variable.

    If you'd like to expand the information collected beyond MPI and include all user-level routines, recompile your application with the –tcollect switch available as part of the Intel® Compilers. In this case, Intel Trace Collector will gather information about all routines in the application, not just MPI. You can similarly filter this via the –tcollect-filter compiler option.

    If you'd like to be explicit about which parts of the code should be profiled, use the Intel Trace Collector API calls. You can manually turn tracing on and off via a quick API call.

    For more Information on all of these methods, refer to the Intel Trace Collector Reference Guide..

  • What file format is the trace data collected in?
  • Intel Trace Collector stores all collected data in Structured Tracefile Format (STF) which allows for better scalability across both time and processes. For more details, refer to the "Structured Tracefile Format" section of Intel Trace Collector Reference Guide.

  • Can I import or export trace data to/from Intel Trace Analyzer and Collector?
  • Yes, you can export the data from any of the Profile charts (Function Profile, Message Profile, and Collective Operations Profile) as part of the Intel Trace Analyzer interface. To do this, open one of these profiles in the GUI, right-click to bring up the Context Menu, and select the "Export Data" option. The data will be saved in simple text format for easy reading.

    At a separate level, you can save your current working Intel Trace Analyzer environment via the Project Menu. If you choose to "Save Project", your current open trace view and associated charts will be recorded as they are open on your screen. You can later choose to "Load Project" from this same menu, which will bring up a previously-saved session.

  • What size MPI application can I analyze with Intel Trace Analyzer and Collector?
  • It depends on how large or complex your application is, how many MPI calls you are making, and for how long you are running. There are no internal limitations on the size of the MPI job but there are plenty of external ones. It all depends on how much memory is available on the system (per core) both for the application, the MPI library, and for the Intel Trace Collector processes, as well as disk space availability. Any additional flags enabled (for example, storing call stack and source code locations) cause an increase in the size of the trace file. Filtering out unimportant information is always a good solution to reducing trace files.

  • How can I control the amount of data collected to a reasonable amount? What is a reasonable amount?
  • Each application is different in terms of the profiling data it can provide. The longer an application runs, and the more MPI calls it makes, the larger the STF files will be. You can filter some of the unnecessary information out by applying appropriate filters (see Question #6 for more details or check out some tips on Intel Trace Collector Filtering).

    Additionally, you can be restricted by the resources allocated to your account; consult your cluster administration about quotas and recommendations.

  • How can I analyze the collected information?
  • Once you have collected the trace data, you can analyze it via the Graphical Interface called the Intel Trace Analyzer. Simply call the command ($ traceanalyzer) or double-click on the Intel Trace Analyzer icon and navigate to your STF files via the File Menu.

    You can get started by opening up the Event Timeline chart (under the Charts Menu) and zooming in at an appropriate level.

    Check out the Detecting and Removing Unnecessary Serialization Tutorial on ideas how to get started. For details on all Intel Trace Analyzer functionality, refer to the Intel Trace Analyzer Reference Guide.

  • Can I use Intel Trace Analyzer and Collector with Intel® VTune™ Amplifier XE, Intel® Inspector XE, or other analysis tools?
  • While these tools would collect information separate from each other, in their own format, it's easy enough to use the Intel VTune Amplifier XE and Intel Inspector XE tools under an MPI environment. Check each tool's respective User's Guide for more info on Viewing Collected MPI Data.

    You can use tools such as Intel VTune Amplifier XE and Intel Inspector XE for node-level analysis, and use the Intel Trace Analyzer and Collector for cluster-level analysis.

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