Google Tech Talks
February, 28 2008
ABSTRACT
Intel recently published more precise memory ordering principles for the
IA32 and Intel Architecture 64 (aka x86) processors. This talk discusses
the ...
Google Tech Talks February, 28 2008
ABSTRACT
Intel recently published more precise memory ordering principles for the IA32 and Intel Architecture 64 (aka x86) processors. This talk discusses the key principles embodied in this memory ordering and explains some of the software driven motivation behind them. Along the way we discuss issues such as publication safety and how to use the principles to implement the memory models found in high level programming languages. The presentation is aimed at developers of concurrent shared memory software and will provide a presentation of the principles as well as guidance on how to reason about them.
This is joint work with Bratin Saha and many others both inside as well as outside Intel.
Speaker: Richard L. Hudson Richard L. Hudson is best known for his work in memory management including the invention of both the Train Algorithm and the Sapphire Algorithm. Richard joined Intel in 1998 where he has worked on memory management, concurrency, synchronization, and memory model related issues. He went to Shortridge, holds a B.A. degree from Hampshire College and an M.S. degree from the University of Massachusetts.
Like to rate videos and let people know what you think?
Automatically share your ratings, favorites, and more on Facebook, Twitter, and Google Reader with YouTube Autoshare.
Autoshare makes certain YouTube activities public on the services you choose. Select only the services you are comfortable with - like Facebook, Twitter, or Google Reader - to let your friends know what you like on YouTube. You can turn Autoshare off at any time.
Like to share videos with friends?
Automatically share your ratings, favorites, and more on Facebook, Twitter, and Google Reader with YouTube Autoshare.
Autoshare makes certain YouTube activities public on the services you choose. Select only the services you are comfortable with - like Facebook, Twitter, or Google Reader - to let your friends know what you like on YouTube. You can turn Autoshare off at any time.
It seems to me that the total lock ordering must impose a fundamental scalability overhead. I haven't read the paper but it seems they're saying that they will supply an actual total ordering, rather than an "as-if" ordering. Meaning that two completely independent processes will get a total ordering on their critical sections, regardless of whether they could detect that or not. It's hard for me to see how a performance impact could be avoided.
Autoshare makes certain YouTube activities public on the services you choose. Select only the services you are comfortable with - like Facebook, Twitter, or Google Reader - to let your friends know what you like on YouTube. You can turn Autoshare off at any time.
Interesting topic btw...
sigue asi! chau.