Intel Trace Analyzer and Collector 7.2
Introduction
Intel Trace Analyzer and Collector bundle together graphical analysis and runtime event traces, providing maximum performance to cluster-application developers. It is a must-have tool to understand complex parallel program behavior.
Analyze, optimize, and deploy high-performance applications on Intel® processor-based clusters. Intel® Trace Analyzer and Collector provide information critical to understanding and optimizing application performance on clusters by quickly finding performance bottlenecks in MPI communication. Version 7.1 now includes trace file comparison, counter data displays, and an MPI correctness checking library.
Features
IMPI Checking
• Included in Intel Trace Analyze and Collector is a unique MPI correctness checker to detect deadlocks, data corruption, or 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 the ability to detect errors even among a large number of processes.
Interface and Displays
• 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 multi-threaded MPI applications for event-based tracing to non-MPI applications.
Instrumentation and Tracing
• Low intrusion instrumentation supports MPI applications with C, C++, or Fortran.
• Automatically records performance data from parallel threads in C, C++, Fortran, or Java multithreaded processes.
Benefits
For parallel application development on cluster systems, Intel® Trace Analyzer and Collector is a powerful tool to understand MPI application behavior and achieve high execution performance.
• 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
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