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| Content Provider | ACM Digital Library |
|---|---|
| Author | Santana, Oliverio J. Ramirez, Alex Valero, Mateo Larriba-Pey, Josep L. |
| Abstract | Fetch performance is a very important factor because it effectively limits the overall processor performance. However, there is little performance advantage in increasing front-end performance beyond what the back-end can consume. For each processor design, the target is to build the best possible fetch engine for the required performance level A fetch engine will be better if it provides better performance, but also if it takes fewer resources, requires less chip area, or consumes less power.In this paper we propose a novel fetch architecture based on the execution of long streams of sequential instructions, taking maximum advantage of code layout optimizations. We describe our architecture in detail, and show that it requires less complexity and resources than other high performance fetch architectures like the trace cache, while providing a high fetch performance suitable for wide-issue super-scalar processors.Our results show that using our fetch architecture and code layout optimizations obtains 10% higher performance than the EV8 fetch architecture, and 4% higher than the FTB architecture using state-of-the-art branch predictors, while being only 1.5% slower than the trace cache. Even in the absence of code layout optimizations, fetching instruction streams is still 10% faster than the EV8, and only 4% slower than the trace cache.Fetching instruction streams effectively exploits the special characteristics of layout optimized codes to provide a high fetch performance, close to that of a trace cache, but has a much lower cost and complexity, similar to that of a basic block architecture. |
| Starting Page | 371 |
| Ending Page | 382 |
| Page Count | 12 |
| File Format | |
| ISBN | 0769518591 |
| ISSN | 10724451 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2002-11-18 |
| Access Restriction | Subscribed |
| Content Type | Text |
| Resource Type | Article |
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