Data decompression is one of the most important techniques in data processing and has been widely used in multimedia information transmission and processing. However, the existing decompression algorithms on multicore platforms are time-consuming and do not support large data well. In order to expand parallelism and enhance decompression efficiency on large-scale datasets, based on the software thread-level speculation technique, this paper raises a speculative parallel decompression algorithm on Apache Spark. By analyzing the data structure of the compressed data, the algorithm firstly hires a function to divide compressed data into blocks which can be decompressed independently and then spawns a number of threads to speculatively decompress data blocks in parallel. At last, the speculative results are merged to form the final outcome. Comparing with the conventional parallel approach on multicore platform, the proposed algorithm is very efficiency and obtains a high parallelism degree by making the best of the resources of the cluster. Experiments show that the proposed approach could achieve 2.6 $$\times $$ × speedup when comparing with the traditional approach in average. In addition, with the growing number of working nodes, the execution time cost decreases gradually, and the speedup scales linearly. The results indicate that the decompression efficiency can be significantly enhanced by adopting this speculative parallel algorithm.
The Journal of Supercomputing – Springer Journals
Published: Mar 21, 2017
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera