How to do performance analysis on your parallelized program efficiently?

Be a scientist: Gather data. Analyze it. Especially when it comes to parallelism and scalability, there’s just no substitute for the advice to measure, measure, measure, and understand what the results mean. Putting together test harnesses and generating and analyzing numbers is work, but the work will reward you with a priceless understanding of how your code actually runs, especially on parallel hardware—an understanding you will never gain from just reading the code or in any other way. And then, at the end, you will ship high-quality parallel code not because you think it’s fast enough, but because you know under what circumstances it is and isn’t (there will always be an “isn’t”), and why.

Herb Sutter

阅读全文>>

Be a scientist: Gather data. Analyze it. Especially when it comes to parallelism and scalability, there’s just no substitute for the advice to measure, measure, measure, and understand what the results mean. Putting together test harnesses and generating and analyzing numbers is work, but the work will reward you with a priceless understanding of how your code actually runs, especially on parallel hardware—an understanding you will never gain from just reading the code or in any other way. And then, at the end, you will ship high-quality parallel code not because you think it’s fast enough, but because you know under what circumstances it is and isn’t (there will always be an “isn’t”), and why.

Herb Sutter

doubanclaim5959b1bcd330f270

发表评论

电子邮件地址不会被公开。 必填项已用*标注