Search results for "Superscalar"
showing 5 items of 5 documents
Using FOCAP tool for teaching microarchitecture simulation and optimization
2013
This paper presents our new developed FOCAP tool (Framework for optimizing the Computer Architecture Performance) in order to gain a better understanding and familiarity of the students with new advanced learning methods and tools in the Microarchitecture Simulation and Optimization. At this stage, FOCAP allows a mono-objective automatic design space exploration (DSE) of a superscalar processor by varying several architectural parameters. Such DSE tools are very useful, since it is impossible to simulate all the configurations of a highly parameterized microarchitecture. Therefore, heuristic methods, local search algorithms and advanced machine learning methods are good candidates to find n…
Multi-objective optimisations for a superscalar architecture with selective value prediction
2012
This work extends an earlier manual design space ex ploration of our developed Selective Load Value Pre diction based superscalar architecture to the L2 unified cache. A fter that we perform an automatic design space expl oration using a special developed software tool by varying several architectural parameters. Our goal is to find optim al configurations in terms of CPI (Cycles per Instruction) and energy consumption. By varying 19 architectural parameter s, as we proposed, the design space is over 2.5 millions of billions configurations which obviously means that only heuristic search can be considered. Therefore, we propose dif ferent methods of automatic design space exploratio n based…
A Comparison of Multi-objective Algorithms for the Automatic Design Space Exploration of a Superscalar System
2013
In today’s computer architectures the design spaces are huge, thus making it very difficult to find optimal configurations. One way to cope with this problem is to use Automatic Design Space Exploration (ADSE) techniques. We developed the Framework for Automatic Design Space Exploration (FADSE) which is focused on microarchitectural optimizations. This framework includes several state-of-the art heuristic algorithms.
Performance and energy optimisation in CPUs through fuzzy knowledge representation
2019
Abstract This paper presents an automatic design space exploration using processor design knowledge for the multi-objective optimisation of a superscalar microarchitecture enhanced with selective load value prediction (SLVP). We introduced new important SLVP parameters and determined their influence regarding performance, energy consumption, and thermal dissipation. We significantly enlarged initial processor design knowledge expressed through fuzzy rules and we analysed its role in the process of automatic design space exploration. The proposed fuzzy rules improve the diversity and quality of solutions, and the convergence speed of the design space exploration process. Experiments show tha…
Exploiting selective instruction reuse and value prediction in a superscalar architecture
2009
In our previously published research we discovered some very difficult to predict branches, called unbiased branches. Since the overall performance of modern processors is seriously affected by misprediction recovery, especially these difficult branches represent a source of important performance penalties. Our statistics show that about 28% of branches are dependent on critical Load instructions. Moreover, 5.61% of branches are unbiased and depend on critical Loads, too. In the same way, about 21% of branches depend on MUL/DIV instructions whereas 3.76% are unbiased and depend on MUL/DIV instructions. These dependences involve high-penalty mispredictions becoming serious performance obstac…