Advisor

Introduction

Intel® Advisor XE is a low-weight threading assistant for C, C++, C# and Fortran. It guides developers through threading design, automating analyses required for fast and correct implementation. It also helps developers to add parallelism to their existing C/C++ or Fortran programs. Apart from thread parallelism, Advisor also supports analyzing MPI-parallel applications. The overall efficiency of an MPI-parallel loop/function can be measured by manually adding individual bandwidths and performances. For example, one runs an application on "n" MPI ranks, attaching Advisor to each rank. Let us assume that for a specific loop/function the bandwidths corresponding to rank-1 to rank-n turn out to be (X1, X2,...,Xn) GB/sec and the respective performances are (Y1, Y2,...,Yn) GF/sec. Then, the total bandwidth would be (X1+X2+...+Xn) GB/sec and total performance (Y1+Y2+...+Yn) GF/sec. Note that, if there is a significant deviation among the values of Xi or Yi (i=1,2,...n), then there is a load imbalance among the ranks and one may add "--mpi-trace" flag to the survey command and repeat the analysis. However, if the load balance among MPI-ranks is good, running Advisor on a single rank would be enough.

Developer: Intel Platforms: Category: Performance Analyzer License: Commercial Website: Intel® Advisor XE homepage

In brief, one may use the Intel Advisor XE to:

• find the most time-consuming serial code regions in your program.
• analyse Roofline plot, which highlights hot functions/loops and suggests necessary optimizations. With the help of Roofline plot, one can confirm whether an application is memory-bound or compute-bound.
• analyse memory access pattern to know if the memory is being accessed in unit stride or non-unit stride.
• explore loop-carried dependencies hindering efficient vectorization.
• find out if there are data-type conversions hindering vectorization in the code.
• estimate the load imbalance and parallel efficiency in an MPI-parallel application.
• insert Intel Advisor XE annotations to identify these as possible parallel code regions.
• predict the approximate parallel performance characteristics of the proposed parallel code regions.
• check for data sharing problems that could prevent the application from working correctly when parallelized.

Slides for a general introduction about the Advisor can be found here.

For the slides on the memory access pattern, one may click here.

Slides on vectorization and dependency can be found here.

For the last three points in the introduction section, one may refer to the tutorial here.

Why Intel Advisor?

Before checking the parallel efficiency of an application, it is necessary to understand how the application behaves at the core and node level. For example,

• whether it is memory bound or compute bound

• how good is the vectorization

• how is the memory access pattern

• whether there are dependencies hindering vectorization

• where different loops/functions lie on the Roofline plot and if there is a room for improvement, etc.

Intel Advisor not only provides answer to all the above-mentioned queries, but also suggests solutions, for example, what kind of optimizations one needs to implement in order to improve the performance of an application.

How to use Intel Advisor?

First, compile your application with the flag "-g" followed by other optimization flags, for example on Hawk "-O2 (or -O3) -march=core-avx2". Then, set up an environment for the Advisor by loading the corresponding module.

For example, on Hawk

 module load advisor

On Vulcan

 module load performance/advisor

If you have installed Intel oneAPI on your laptop then,

 source /opt/intel/oneapi/setvars.sh

Running Advisor on OpenMP parallel application

Select the number of OpenMP threads as,

 export OMP_NUM_THREADS=num_of_threads

and bind them as,

 export OMP_PROC_BIND=spread

Afterwards, collect survey, tripcounts and flops as follows,

  advixe-cl -collect survey -project-dir results_advisor  ./a.out

  advixe-cl -collect tripcounts -flop -project-dir results_advisor  ./a.out

Here, survey is an internal tool which locates non-vectorized and poorly vectorized loops/functions and estimates performance gain with efficient vectorization. Trip counts introduces counters to measure time spent in a particular loop/function, and the flag “-flop” enables the flop counter.

Results can be visualized using Advisor GUI,

  advixe-gui results_advisor/e000/e000.advixeexp

Visualizing results on Hawk could be slow if the GUI contents are being transferred via thin DSL lines. Therefore, one shall try using VNC for the purpose of visualization. Alternatively, one may pack up all the results in a read-only file as follows

  advixe-cl --snapshot --project-dir=results_advisor --cache-sources path_to_source_code --cache-binaries path_to_binary

Above command will create a file snapshot000.advixeexpz which requires very less memory as compared to the original results_advisor directory and thus can be easily copied to the local machine. The file can be viewed in GUI as,

  advixe-gui snapshot000.advixeexpz

Running Advisor on MPI parallel application

In order to get useful information from the Advisor analysis, it is necessary to pin MPI ranks according to the machine hardware. This is possible only when command line arguments are exported as variables. For example, if one would like to use OpenMPI and want to distribute 8 MPI ranks uniformly over the Hawk node. This can be achieved with the following steps:

Export number of MPI ranks

export NUM_MPI=8

Then export pinning in a variable

export MPIRUN_OPTIONS="--bind-to cpu-list:ordered --cpu-list 0,16,32,48,64,80,96,112 -report-bindings"

Export survey and tripcounts command as follows

export ADVISOR_SURVEY="advixe-cl -collect survey -project-dir results_advisor"

export ADVISOR_TRIPCOUNTS="advixe-cl -collect tripcounts -flop -project-dir ${SCR}/results_advisor"

Now collect survey and tripcounts as follows,

mpirun -np ${NUM_MPI} ${MPIRUN_OPTIONS} ${ADVISOR_SURVEY} ./a.out 

wait

mpirun -np ${NUM_MPI} ${MPIRUN_OPTIONS} ${ADVISOR_TRIPCOUNTS}  ./a.out

Collect survey, tripcounts and flops as follows,

  mpirun -np $num_of_mpi_tasks advixe-cl -collect survey -project-dir results_advisor  ./a.out

  mpirun -np $num_of_mpi_tasks advixe-cl -collect tripcounts -flop -project-dir results_advisor  ./a.out

Above command will create Advisor reports for all the ranks. In case, one would like to run Advisor only on the single rank, then do the following,

  mpirun -np $num_of_mpi_tasks-1 ./a.out : -np 1 advixe-cl -collect survey -project-dir results_advisor  ./a.out

  mpirun -np $num_of_mpi_tasks-1 ./a.out : -np 1 advixe-cl -collect tripcounts -flop -project-dir results_advisor  ./a.out

Running Advisor on MPI+OpenMP parallel application

The following example employs 32 MPI tasks distributed uniformly over both the sockets with 2 OpenMP threads per MPI tasks on a Hawk node.

  module load mpt
  export MPI_SHEPHERD=1 
  export MPI_DSM_CPULIST=0-127/2:allhosts
  export OMP_NUM_THREADS=2
  export OMP_PROC_BIND=close
  export MPI_OPENMP_INTEROP=1

One can then run Advisor, same as described in the above section.

  mpirun -np $num_of_mpi_tasks-1 ./a.out : -np 1 advixe-cl -collect survey -project-dir results_advisor  ./a.out

  mpirun -np $num_of_mpi_tasks-1 ./a.out : -np 1 advixe-cl -collect tripcounts -flop -project-dir results_advisor  ./a.out

Additional analysis - memory access pattern and dependencies

While visualizing the results, Advisor might suggest performing additional analysis like memory access pattern and dependencies. One may collect the same, for example, as follows,

  mpirun -np $num_of_mpi_tasks-1 ./a.out : -np 1 advixe-cl -collect map -project-dir results_advisor  ./a.out

  mpirun -np $num_of_mpi_tasks-1 ./a.out : -np 1 advixe-cl -collect dependencies -project-dir results_advisor  ./a.out

Note that above analysis is possible only after collecting survey and tripcounts.

See also

• Software Development Tools, Compilers & Libraries

External links

• Intel® Advisor XE homepage
• Intel® Advisor XE Documenation