Netlogo HPC CPU百分比使用增加 [英] Netlogo HPC CPU Percentage Use Increase

问题描述

我使用无头NetLogo将作业通过以下代码提交给HPC服务器：

I submit jobs using headless NetLogo to a HPC server by the following code:

#!/bin/bash
#$ -N r20p
#$ -q all.q
#$ -pe mpi 24
/home/abhishekb/netlogo/netlogo-5.1.0/netlogo-headless.sh \
    --model /home/abhishekb/models/corrected-rk4-20presults.nlogo \
    --experiment test \
    --table /home/abhishekb/csvresults/corrected-rk4-20presults.csv

/ strong>的群集队列：

Below is the snapshot of a cluster queue using:

qstat -g c

>

我想知道我可以为我的模拟增加CQLOAD，它也意味着什么。我无法在线找到一个澄清的解释。

I wish to know can I increase the CQLOAD for my simulations and what does it signify too. I couldn't find an elucidate explanation online.

CPU使用检查：

qhost -u abhishekb

当我通过gui分配在PC上运行行为空间时高优先级的任务使它使用接近99％的CPU，使其运行更快。它使用更大百分比的CPU处理器。我希望在这里完成同样的工作。

When I run the behaviour space on my PC through gui assigning high priority to the task makes it use nearly 99% of the CPU which makes it run faster. It uses a greater percentage of CPU processor. I wish to accomplish the same here.

EDIT：

编辑2;

推荐答案

典型的HPC环境设计为每个CPU核心只运行一个MPI进程（或OpenMP线程），因此可以访问100％的CPU时间，而且无法进一步提高。相比之下，在经典的桌面/服务器机器上，多个进程竞争CPU时间，并且确实可以通过 nice 。

A typical HPC environment, is designed to run only one MPI process (or OpenMP thread) per CPU core, which has therefore access to 100% of CPU time, and this cannot be increased further. In contrast, on a classical desktop/server machine, a number of processes compete for CPU time, and it is indeed possible to increase performance of one of them by setting the appropriate priorities with nice.

看来CQLOAD是该计算队列的平均负载平均值。如果您不使用其中的所有CPU核心，它不是一个有用的指标。此外，即使您的运行的每个核心的负载平均值仅转换此HPC集群上的代码的效率。例如，每个核心0.7的值意味着代码花费70％的时间进行计算，而剩余的30％可能花费在等待与其他计算节点通信（这也是必要的）。

It appears that CQLOAD, is the mean load average for that computing queue. If you are not using all the CPU cores in it, it is not a useful indicator. Besides, even the load average per core for your runs just translates the efficiency of the code on this HPC cluster. For instance, a value of 0.7 per core, would mean that the code spends 70% of time doing calculations, while the remaining 30% are probably spent waiting to communicate with the other computing nodes (which is also necessary).

底线，您可以增加HPC集群上CPU使用率的唯一方法是优化代码。通常，人们更关心并行缩放（即解决时间如何随着CPU核心数量而减少）比使用CPU百分比。

Bottom line, the only way you can increase the CPU percentage use on an HPC cluster is by optimising your code. Normally though, people are more concerned about the parallel scaling (i.e. how the time to solution decreases with the number of CPU cores) than with the CPU percentage use.

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