Command:mpirun -n 24 ./mmult3_c.exe 4608
Resources:1 node (12 physical, 24 logical cores per node)
Tasks:24 processes
Machine:mic2
Start time:Fri Feb 20 21:29:52 2015
Total time:60 seconds (1 minute)
Full path:/scratch/allinea/mmult/3_fix
Input file:
Notes:

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Summary: mmult3_c.exe is MPI-bound in this configuration
CPU44.9%

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

This is low; it may be worth improving MPI or I/O performance first.

MPI54.9%

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

This is high; check the MPI breakdown for advice on reducing it.

I/O0.2%

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

This is very low; however single-process I/O often causes large MPI wait times.

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

CPU
A breakdown of the 44.9% CPU time:
Scalar numeric ops11.2%
Vector numeric ops7.5%
Memory accesses81.3%
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.
MPI
A breakdown of the 54.9% MPI time:
Time in collective calls69.0%
Time in point-to-point calls31.0%
Effective process collective rate0.00e+00 
Effective process point-to-point rate3.42e+07 
Most of the time is spent in collective calls with a very low transfer rate. This suggests load imbalance is causing synchonization overhead; use an MPI profiler to investigate further.
The point-to-point transfer rate is low. This can be caused by inefficient message sizes, such as many small messages, or by imbalanced workloads causing processes to wait.
I/O
A breakdown of the 0.2% I/O time:
Time in reads0.0%
Time in writes100.0%
Effective process read rate0.00e+00 
Effective process write rate1.09e+08 
Most of the time is spent in write operations with an average effective transfer rate. It may be possible to achieve faster effective transfer rates using asynchronous file operations.
Threads
A breakdown of how multiple threads were used:
Computation0.0%
Synchronization0.0%
Physical core utilization199.7%
Involuntary context switches per second373239.4
No measurable time is spent in multithreaded code.
Memory
Per-process memory usage may also affect scaling:
Mean process memory usage1.62e+08 
Peak process memory usage5.58e+08 
Peak node memory usage32.0%
There is significant variation between peak and mean memory usage. This may be a sign of workload imbalance or a memory leak.
The peak node memory usage is low. Running with fewer MPI processes and more data on each process may be more efficient.