#Length of a vector 2d full

The second phase is to run a set of tasks where each task compares a partition of vectors with other candidate partitions. The first phase is to partition the data and group vectors that are potentially similar. This paper studies a scalable two-phase approach called Partition-based Similarity Search (PSS). However, shuffling intermediate results can incur significant communication overhead as data scales up. Previous work has used techniques such as comparison filtering, inverted indexing, and parallel accumulation of partial results. Results shows excellent efficiency and scalability when scaling to 96 GPUs, even obtaining super-linear speedups due to a distributed cache.Īll pairs similarity search (APSS) is used in many web search and data mining applications. We evaluate our solution using three real-world applications (from digital forensics, localization microscopy, and bioinformatics) on different platforms (from a desktop machine to a supercomputer). In contrast, we present a solution that relies on hierarchical multi-level software-based caches to maximize data reuse at each level in the distributed memory hierarchy, combined with a divide-and-conquer approach to exploit data locality, hierarchical work-stealing to dynamically balance the workload, and asynchronous processing to maximize resource utilization.

#Length of a vector 2d full

Several researchers considered this problem, either resorting to partial replication with static work distribution or dynamic scheduling with full replication. Although these problems present an abundance of parallelism, data reuse must be exploited to achieve good performance. All-pairs compute problems apply a user-defined function to each combination of two items of a given data set.