![]() ![]() The paper presents experimental results that show how each performance factor is affected by the selection of algorithm, changes in the input data-set and variations in architectural characteristics such as cache capacity and degree of cache sharing. The study considers two classes of algorithms and several algorithmic variants and evaluates each implementation based on a variety of performance metrics including data locality and sharing, granularity of parallelism and scalability. The UKP is very easy to solve (despite being weakly NP-Hard). So you can solve each knapsack separately (losing some shared effort). This paper investigates the impact of algorithmic choice on the performance of parallel implementations of the integral knapsack problem on multicore architectures. variables, factoring polynomials over the rationals, breaking knapsack based. As the number of items is unbounded, what you put in one knapsack does not affect the others. Hence, in the era of multicore computing, it is imperative that we re-evaluate and rethink algorithms for key problem domains. Unbounded Knapsack (Repetition of items allowed) Given a knapsack weight W and a set of n items with certain value vali and weight wti, we need to calculate minimum amount that could make up this. Additionally, since the amount of parallelism extracted by a compiler is directly influenced by the selection of the algorithm, algorithmic choice also plays a critical role in achieving a high fraction of peak performance. ![]() However, harnessing the full potential of these systems depends largely on the effectiveness of system software, such as compilers, in exploiting the on-chip parallelism. This is why we allow the books compilations in this website.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |