Atnaujintas knygų su minimaliais defektais pasiūlymas! Naršykite ČIA >>
We have proposed mechanisms to implement function memoization at a software level as part of our effort to improve sequential code performance. We have analyzed the potential of function memoization on applications and its performance gain on current architectures. We have proposed three schemes ¿ a simple load time approach which works for any dynamically linked function, a compile time approach using LLVM framework which can enable memoization for any program function and also a hardware proposal for doing memoization in hardware and its potential benefits. Demonstration of the link time approach with transcendental functions showed that memoization is applicable and gives good benefit even under modern architectures and compilers (with the restriction that it can be applied only for dynamically linked functions). Our compile time approach extends the scope of memoization and also increases the benefit due to memoization. This works for both user defined functions as well as library functions. It can handle certain kind of non pure functions like those functions with pointer arguments and global variable usage.
| Autorius: | Arjun Suresh |
| Leidėjas: | Éditions universitaires européennes |
| Išleidimo metai: | 2017 |
| Knygos puslapių skaičius: | 132 |
| ISBN-10: | 3841666124 |
| ISBN-13: | 9783841666123 |
| Formatas: | 220 x 150 x 8 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Intercepting Functions for Memoization“
