Best-effort semantic document search on GPUs

Semantic indexing is a popular technique used to access and organize large amounts of unstructured text data. We describe an optimized implementation of semantic indexing and document search on manycore GPU platforms. We observed that a parallel implementation of semantic indexing on a 128-core Tesla C870 GPU is only 2.4X faster than a sequential implementation on an Intel Xeon 2.4GHz processor. We ascribe the less than spectacular speedup to a mismatch in the workload characteristics of semantic indexing and the unique architectural features of GPUs. Compared to the regular numerical computations that have been ported to GPUs with great success, our semantic indexing algorithm (the recently proposed Supervised Semantic Indexing algorithm called SSI) has interesting characteristics – the amount of parallelism in each training instance is data-dependent, and each iteration involves the product of a dense matrix with a sparse vector, resulting in random memory access patterns. As a re...

• Type: Journal
• Year: 2010
• Where: ASPLOS
• Authors: Surendra Byna, Jiayuan Meng, Anand Raghunathan, Srimat T. Chakradhar, Srihari Cadambi
• Keywords: ASPLOS 2010, Gpu Platform, Programming Languages, Semantic Indexing, Semantic Indexing Algorithm