ray_with_allinea_performance_reports.md 1.64 KB
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title = "Ray with Allinea Performance Reports"
description = "Example of how to profile Ray using Allinea Performance Reports"
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Simple example of using [Ray]({{< relref "/applications/app_specific/bioinformatics_tools/de_novo_assembly_tools/ray" >}})
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with Allinea PerformanceReports (`perf-report`) is shown below:
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{{% panel theme="info" header="ray_perf_report.submit" %}}
{{< highlight batch >}}
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#!/bin/bash
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#SBATCH --job-name=Ray
#SBATCH --ntasks-per-node=16
#SBATCH --time=10:00:00
#SBATCH --mem=70gb
#SBATCH --output=Ray.info
#SBATCH --error=Ray.error

module load allinea
module load compiler/gcc/4.7 openmpi/1.6 ray/2.3

perf-report mpiexec -n 16 Ray -k 31 -p -p input_reads_pair_1.fasta input_reads\_pair_2.fasta -o output_directory
{{< /highlight >}}
{{% /panel %}}

Ray is MPI and therefore additional Allinea Performance Reports options
are not required. The `perf-report` command is placed in front of the
actual `Ray` command we want to analyze.

When the application finishes, the performance report is generated in
the working directory.
For the executed application, this is how the report looks like:

{{< figure src="/images/11635303.png" width="850" >}}

From the report, we can see that **Ray **is Compute-Bound application.
Most of the running time is spent in point-to-point calls with a low
transfer rate which may be caused by inefficient message sizes.
Therefore, running this application with fewer MPI processes and more
data on each process may be more efficient.

Running Allinea Performance Reports and identifying application
bottlenecks is really useful for improving the application and better
utilization of the available resources.