Re: TMS Instrumentation Workshop V2.0.0.0 Full Source
Tanesha Prately
TAU (Tuning and Analysis Utilities) is a comprehensive profiling and tracing toolkit for performance analysis of parallel programs written in Fortran, C, C++, Java, and Python. It is capable of gathering performance information through instrumentation of functions, methods, basic blocks, and statements. All C++ language features are supported including templates and namespaces. The instrumentation consists of calls to TAU library routines, which can be incorporated into a program in several ways:
The easiest and quickest way to profile an application is to use the tau_exec command. It automatically instruments your executable at run time, and requires no special compilation or modifications to source code. All you need to do is make sure your TAU environment is setup correctly.
1. Setup your TAU environment by loading the TAU module. Also, just to be sure, set the TAU_PROFILE environment variable to "1". Optionally, you can specify where the profile files are written (default is working directory).
Another way to automatically instrument your application is to use the TAU Makefile scripts. This is slightly more work, but is required for profiling some parameters, such as hardware counter (PAPI) events.
5. Compile your application using the appropriate TAU compiler wrapper script. These are located in the /bin directory of the TAU package you loaded, and should be in your path. The choices are shown in the table below. Note that if you are using makefiles, you will need to substitute these wrapper scripts accordingly.
Note that compiler options will get passed to the native compiler of your choice. Also note that TAU provides a number of its own compiler options, not discussed here. For details, see TAU Compiler Options.
TAU can be used to trace events during a program's execution. Unlike profiling, which aggregates the time spent in each routine, loop, etc. tracing allows you to view events as they relate to each other against a timeline. One caveat about tracing however, is that trace files can quickly grow to be very large, which makes tracing difficult or impossible for long running, many process jobs.
As with profiling, the easiest and quickest way to trace an application is to use the tau_exec command. It automatically instruments your executable at run time, and requires no special compilation or modifications to source code. All you need to do is make sure your TAU environment is setup correctly.
1. Setup your TAU environment by loading the TAU module. Also, make sure the TAU_TRACE environment variable is set to "1". If you want to specify a directory where the tracefiles should be written (default is the working directory), use the TRACEDIR environment variable.
1. First, setup your TAU environment by loading the TAU module. Also, make sure the TAU_TRACE environment variable is set to "1". If you want to specify a directory where the tracefiles should be written (default is the working directory), use the TRACEDIR environment variable.
3. There are many counters available, but in practice, you can usually only use a few at a time, because not all events can be counted together. Decide which events you want to count, and then find out if they are compatible or not with the papi_event_chooser command. The example below shows an incompatibility.
5. Run your application as usual. Following execution, you will have a unique directory for each PAPI event. Inside each directory, there will be a set of files named profile.#.* where # denotes the MPI rank. Viewing these files is discussed in the Output section below.
Note: you can perform tracing at the same time as recording PAPI events by setting the TAU_TRACE environment variable to "1". You cannot, however perform normal TAU profiling at the same time as PAPI.
TAU provides the ability for users to customize their application's instrumentation, thereby enabling them to focus on specific areas of interest, and reduce run-time overhead associated with profiling the entire application. There are two ways to do this, as discussed below.
1. Create a text file that contains the names of routines and/or source files that should be instrumented or not instrumented. The type of instrumentation can also be specified: loops, memory, I/O, etc.
The TAU Instrumentation API provides a means for users to place TAU routines in their source code to explicitly direct how TAU should instrument their application. There are over 125 routines available. For details, see the TAU documentation at www.cs.uoregon.edu/research/tau/docs/newguide/bk03rn01.html.
To get a quick, text based summary of your job's profile data, the TAU pprof utility can be used. By default, it will process all of the profile.* files in the current directory and produce a report showing profile data for each rank/context/thread. An example for one MPI rank is shown below:
ParaProf includes many features for diving more deeply into your application's behavior - see the TAU Documentation for details. A few representative screenshots are provided below (click for a larger image).
Before TAU's trace files can be viewed, they must first be merged (for parallel jobs) and then converted to a suitable format for viewing by selected trace viewing tools. Two trace viewing tools are covered here.
The Vampir trace visualizer provides a variety of means of examining OTF trace data, as generated through VampirTrace, OpenSpeedShop, or TAU. VampirServer is a client/server version of Vampir that can quickly extract and analyze data from large trace files by using a parallel backend. See the Vampir documentation for details.
As discussed in the Quick Start section above, TAU can be used to instrument applications without any special need to compile or link. However, using some TAU features does require compiling and linking with TAU components. For the most part, all of this is accomplished by using TAU Makefiles and compiler wrappers as described in steps 1 through 5 under Profiling with TAU Makefiles. Additional information can be found in the TAU documentation.
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Energize Your Environmental Science Class with the CastAway-CTD
Presenters: SonTek Application Engineer, Dr. Xue Fan, and special guest from the Scripps Institution of Oceanography, Dr. Todd Martz.
The CastAway-CTD is a widely used tool in environmental science classes as a way to help instructors teach water property fundamentals and demonstrate methods to collect data. Today, educators have even more resources with the NEW customized lesson plan focusing on salinity measurements, now available with all CastAway-CTDs! (Original Air Date: Dec 12, 2018)
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The SME Instrument was set up under the Horizon 2020 research framework programme to support innovation in small and medium-sized enterprises (SMEs). Its objective is to develop and capitalise on the potential of SMEs by filling the gap in funding for early stage high-risk projects and increasing private-sector commercialisation of research results. It is targeted towards innovative SMEs in the EU and 16 associated countries that show strong ambition to develop, grow and internationalise in all different types of innovation.
We found that the SME Instrument provides effective support to SMEs in developing their innovation projects and that having the EU branding that comes from receiving EU support helps companies to attract additional investment.
We found that improvements have been made to evaluation and selection procedures during the lifetime of the instrument, with the oral presentation of project proposals to a panel of jury members being a particularly useful addition in identifying the best proposals. However, a two-way feedback between the evaluation stages is missing. In addition, some IT tools are not fit for purpose, thus undermining the process which is already under pressure.
The resubmission of unsuccessful proposals is a large and increasing drain on management and evaluation resources without providing added value. Not only does it increase administrative costs, it also lowers the success rate, thus discouraging participation.
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