There's no need to instrument your code or remember arcane compilation settings. Just compile your code with -g and start Allinea MAP as you would mpiexec:

    $ map -n 128 ./bt_128_C datafile.in

Simple. Allinea MAP shares the same batch queue integration as Allinea DDT, which means it will happily submit jobs for you and export the data when they are done.

After your program finishes, Allinea MAP shows you the lines of source code that took the longest. With time spent computing in green and communicating in blue, it's the clearest way to see what actually happened during the run.

Built on Allinea DDT's industry-leading GUI, many of the Allinea MAP views will be familiar - the source code, the parallel stack view and quick-open with autocomplete for file navigation all integrate seamlessly with brand new performance-specific views designed by the same team.

It all just works, as you'd expect from the leaders in HPC development tools.

Unlike the classic trace-based generation of performance tools, Allinea MAP never drowns you or your file system in data.

Adaptive sampling rates combined with Allinea DDT's class-leading on-cluster merge technology ensure that exactly the right amount of data is recorded - whether you run on a workstation for ten minutes or on a remote supercomputer with tens of thousands of processes for a week.

With Allinea MAP you never need to worry about whether you chose the right metrics or level of implementation. Everything is turned on, all the time, with just 5% wall-clock overhead.

Everything about Allinea MAP is designed to get out of your way and show the performance of your code. From the default selection of views to the adaptive sampling rate, we strive to take decisions about how to run a profiler away - leaving all the really interesting decisions about your code.

We haven't confused simplicity of use with shallowness, though. Allinea MAP lets you drill deep down into the performance of your code:

• Check memory usage, floating-point calculations and MPI usage at a glance
• Flick to the CPU view to see the percentage of vectorized SIMD instructions, including AVX extensions used in each part of the code
• See how the amount of time spent in memory operations varies over time and processes - are you making efficient use of the cache?
• Zoom in to any part of the timeline, isolate a single iteration and explore its behaviour in detail
• Everything shows aggregated data, preferring distributions with outlying ranks labelled to endless lists of processes and threads, ensuring the display is as visually scalable as our industry-leading backend is.