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:

  • Fast, light weight, scalable MPI performance analysis
  • 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 Collector Filtering
Por James Tullos (Intel)Publicado en 03/14/20130
Filtering in the Intel® Trace Collector will apply specified filters to the trace collection process.  This directly reduces the amount of data collected.  The filter rules can be applied either via command line arguments or in a configuration file (specified by the environment variable VT_CONFIG...
Intel® MPI correctness checking library
Por Gergana Slavova (Intel)Publicado en 06/19/20090
Find out about the Intel® MPI correctness checking library - a novel technology to detect errors with data types, buffers, communicators, point-to-point messages and collective operations, deadlocks, or data corruption in your MPI programs.
Support for other MPI implementations
Por Gergana Slavova (Intel)Publicado en 06/19/20091
Does the Intel Trace Analyzer and Collector support different MPI implementation? Yes, it supports Intel MPI Library, MPICH, and any MPICH-based implementations. Find out how to switch between those libraries.
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Supplemental Documentation

Intel® Trace Analyzer and Collector 8.1 Update 3 Readme
Por Gergana Slavova (Intel)Publicado en 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...
Intel® Trace Analyzer and Collector 8.1 Update 2 Readme
Por Gergana Slavova (Intel)Publicado en 06/07/20130
The Intel® Trace Analyzer and Collector 8.1 Update 2 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 develo...
Intel® Trace Analyzer and Collector 8.1 Update 1 Readme
Por Gergana Slavova (Intel)Publicado en 04/05/20130
The Intel® Trace Analyzer and Collector 8.1 Update 1 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 ...
Intel® Trace Analyzer and Collector Guides
Por James Tullos (Intel)Publicado en 03/15/20130
This is currently a placeholder for Intel® Trace Analyzer and Collector usage guides.  Until articles are added, please visit the Intel® Trace Analyzer and Collector product page.  You can also view the documentation.
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Intel MPI, perhost, and SLURM: Can I override SLURM?
Por thematt2
All, (Note: I'm also asking this on the slurm-dev list.) I'm hoping you can help me with a question. Namely, I'm on a cluster that uses SLURM and lets say I ask for 2 28-core Haswell nodes to run interactively and I get them. Great, so my environment now has things like: SLURM_NTASKS_PER_NODE=28 SLURM_TASKS_PER_NODE=28(x2) SLURM_JOB_CPUS_PER_NODE=28(x2) SLURM_CPUS_ON_NODE=28 Now, let's run a simple HelloWorld on, say, 48 processors (and pipe through sort to see things a bit better): (1047) $ mpirun -np 48 -print-rank-map ./helloWorld.exe | sort -k2 -g srun.slurm: cluster configuration lacks support for cpu binding (borgj102:0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27) (borgj105:28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47) Process 0 of 48 is on borgj102 Process 1 of 48 is on borgj102 Process 2 of 48 is on borgj102 Process 3 of 48 is on borgj102 Process 4 of 48 is on borgj102 Process 5 of 48 is on borgj102 Pro...
mpitune get "could not dump the session, because unknown encoding: utf-8"
Por Xinwei X. (Intel)1
Hi forum, I try the following command on a server: (impi 5.0.2.044, icc 2015.2.164) mpitune -of analysis.conf -application \"mpirun -n 24 -host `hostname` ./myexe\" It did run for a while but output nothing of analysis.conf. Meanwhile the console output message like: ERR | Could not dump the session, because unknown encoding: utf-8 I try to change LANG=C, as locale outputs: LANG=C LC_CTYPE="C" ...(all other environment variables are "C") How can I run successfully of mpitune to get analysis.conf which has real contents. Thanks!
MPI_Comm_spawn seg fault when used with MPI_Info_set
Por dlinenbe1
Hello, I am a licensed user of Intel MPI for Windows. (5.0.3)   64 bit, Windows 7 I am using MPI_Comm_spawn, and the program crashes when I use MPI_Info_set. This can be seen as follows: Master.exe (root node) #include "mpi.h"  #include <stdio.h> #include<Windows.h> int main( int argc, char **argv )  {      int intercomm;     MPI_Init(&argc, &argv);      MPI_Info info;     MPI_Info_create(&info);     MPI_Info_set(info, "host", "localhost");           // Crashes with host set to localhost,  If this line is commented out, program works fine     MPI_Comm_spawn("hello.exe", MPI_ARGV_NULL, 2, info, 0, MPI_COMM_SELF, &intercomm, MPI_ERRCODES_IGNORE);  //<-- Intel MPI crashes      Sleep(10000);     MPI_Finalize(); }  The slave executable: (Hello.exe) int main_(int argc, char **argv) {     MPI_Init(&argc, &argv);     printf("helloworld\n");      fflush(stdout);     Sleep(10000); } I run with mpiexec -delegate -n 1 Master.exe The prog...
Intel MPI 5.0.2.044 and Windows firewall on localhost
Por Frank R.0
Hi everybody, We run mpiexec on Windows 7 on multiple network installations of our product in the following manner: mpiexec.hydra.exe -np x -localroot -delegate -localonly -localhost 127.0.0.1 one_path_to_EXECUTABLE The problem is with the windows firewall (which is executable path triggering) and alerts, because mpiexec.hydra.exe and pmi_proxy.exe do a listen on 0.0.0.0:port. Is there an option / environment variable available to tell mpiexec.hydra only listen on 127.0.0.1 instead of all available interfaces if we run it only local on one workstation. Thanks in advance Frank
open_hca: getaddr_netdev ERROR: Connection refused. Is ib0 configured ERROR
Por 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
Por 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
Por 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)
Por René O.1
Hello, there is a problem with the latest MPI library and multi-rail support over OFA:
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  • 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.

Intel® Trace Analyzer & Collector

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