Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: On multi-core processors, applications are run sharing the cache.  presents optimization theory to co-locate applications to minimize cache interference and maximize performance. The theory precisely specifies MRC-based composition, optimization, and correctness conditions. The paper also presents a new technique called  footprint symbiosis  to obtain the best shared cache performance under fair CPU allocation as ll as a new sampling technique which reduces the cost of locality analysis. When sampling and optimization are combined, the paper shows that it takes less than 0.1 second analysis per program to obtain a co-run that is within 1.5 percent of the best possible performance. In an exhaustive evaluation with 12,870 tests, the best prior work improves co-run performance by 56 percent on average. The new optimization improves it by another 29 percent. Without single co-run test, footprint symbiosis is able to choose co-run choices that are just 8 percent slor than the best co-run solutions found with exhaustive testing.
['Xiameng Hu', 'Xiaolin Wang', 'Yechen Li', 'Yingwei Luo', 'Chen Ding', 'Zhenlin Wang']
I am writing a research paper on a idea Optimal Symbiosis and Fair Scheduling in Shared Cache
[{"from": "human", "value": "I am writing a research paper on a idea Optimal Symbiosis and Fair Scheduling in Shared Cache"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: On multi-core processors, applications are run sharing the cache.  presents optimization theory to co-locate applications to minimize cache interference and maximize performance. The theory precisely specifies MRC-based composition, optimization, and correctness conditions. The paper also presents a new technique called  footprint symbiosis  to obtain the best shared cache performance under fair CPU allocation as ll as a new sampling technique which reduces the cost of locality analysis. When sampling and optimization are combined, the paper shows that it takes less than 0.1 second analysis per program to obtain a co-run that is within 1.5 percent of the best possible performance. In an exhaustive evaluation with 12,870 tests, the best prior work improves co-run performance by 56 percent on average. The new optimization improves it by another 29 percent. Without single co-run test, footprint symbiosis is able to choose co-run choices that are just 8 percent slor than the best co-run solutions found with exhaustive testing."}]