为什么多线程Java程序在“超级”Linux服务器和笔记本电脑Win7上的速度不快？

Question

Intro

So far, I have been working on a piece of software which I am now testing to see the benefit of concurrency. I am testing the same software using two different systems:

• System 1: 2 x Intel(R) Xeon(R) CPU E5-2665 @ 2.40GHz with a total of 16 cores , 64GB of RAM running on Scientific LINUX 6.1 and JAVA SE Runtime Enviroment (build 1.7.0_11-b21).
• System 2 Lenovo Thinkpad T410 with Intel i5 processor @ 2.67GHz with 4 cores, 4GB of ram running windows 7 64-bit and JAVA SE Runtime Enviroment (build 1.7.0_11-b21).

Details: The program simulates patients with type 1 diabetes. It does some import (read from csv), some numerical computations(Dopri54 + newton) and some export (Write to csv).

I have exclusive rights to the server, so there should be no noise at all.

Results These are my results:

Now as you can see system 1 is just as fast as system 2 despite it is a pretty powerfull machine. I have no idea why this is the case - and I am confident that the system is the same. The number of threads goes from 10-100.

Question:

Why would does the two runs have similar execution time despite system 1 being significantly more powerfull than system 2?

UPDATE!

Now, I just thought a bit about what you guys said about it being an I/O memory issue. So, I thought that if I could reduce the file size it would speed up the program, right? I managed to reduce the import file size with a factor of 5, however, no performance improvement at all. Do you guys still think it is the same problem?

Answer 1

当您编写.csv文件时，瓶颈可能不是您的换档能力，而是硬盘上的写入速率。

Answer 2

Almost certainly this means that either CPU time is not the bottleneck for this application, or that something about it is making it resistant to effective parallelization, or both.

For example if reading the data from disk is actually the limiting factor then faster disks are what matters, not faster processors.

If it's running out of memory then that will be a bigger bottlneck.

If it takes more time to spawn each thread than the actual processing inside the thread.

etc.

In this sort of optimization work metrics are king. You need real hard solid numbers for how long things are taking, and where in your program you are losing that time. Only then can you see where to focus your efforts and see if they are effective.