Command:mpiexec.hydra -bootstrap slurm /home/cbasu/mpprun_tutorial/050416/jacobi_mpi_pr
Resources:1 node (16 physical, 16 logical cores per node)
Memory:31 GB per node
Tasks:16 processes
Start time:Tue Apr 5 13:54:34 2016
Total time:55 seconds (1 minute)
Full path:/home/cbasu/mpprun_tutorial/050416
Input file:

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Summary: jacobi_mpi_pr is Compute-bound in this configuration

Time spent running application code. High values are usually good.

This is very high; check the CPU performance section for advice.


Time spent in MPI calls. High values are usually bad.

This is very low; this code may benefit from a higher process count.


Time spent in filesystem I/O. High values are usually bad.

This is negligible; there's no need to investigate I/O performance.

This application run was Compute-bound. A breakdown of this time and advice for investigating further is in the CPU section below.

As very little time is spent in MPI calls, this code may also benefit from running at larger scales.

A breakdown of the 97.9% CPU time:
Scalar numeric ops13.7%
Vector numeric ops13.5%
Memory accesses72.8%
The per-core performance is memory-bound. Use a profiler to identify time-consuming loops and check their cache performance.
Little time is spent in vectorized instructions. Check the compiler's vectorization advice to see why key loops could not be vectorized.
A breakdown of the 2.1% MPI time:
Time in collective calls65.1%
Time in point-to-point calls34.9%
Effective process collective rate1.07e+04 
Effective process point-to-point rate3.9e+08 
Most of the time is spent in collective calls with a very low transfer rate. This suggests load imbalance is causing synchronization overhead; use an MPI profiler to investigate.
A breakdown of the 0.0% I/O time:
Time in reads0.0%
Time in writes0.0%
Effective process read rate0 
Effective process write rate0 
No time is spent in I/O operations. There's nothing to optimize here!
A breakdown of how multiple threads were used:
Physical core utilization100.0%
System load101.1%
No measurable time is spent in multithreaded code.
Per-process memory usage may also affect scaling:
Mean process memory usage1.07e+08 
Peak process memory usage1.07e+08 
Peak node memory usage10.0%
The peak node memory usage is very low. Running with fewer MPI processes and more data on each process may be more efficient.