Paper Info
Title
G-Tadoc: Enabling Efficient Gpu-Based Text Analytics Without Decompression
Abstract
Text analytics directly on compression (TADOC) has proven to be a promising technology for big data analytics. GPUs are extremely popular accelerators for data analytics systems. Unfortunately, no work so far shows how to utilize GPUs to accelerate TADOC. We describe G-TADOC, the first framework that provides GPU-based text analytics directly on compression, effectively enabling efficient text analytics on GPUs without decompressing the input data.G-TADOC solves three major challenges. First, TADOC involves a large amount of dependencies, which makes it difficult to exploit massive parallelism on a GPU. We develop a novel fine-grained thread-level workload scheduling strategy for GPU threads, which partitions heavily-dependent loads adaptively in a fine-grained manner. Second, in developing G-TADOC, thousands of GPU threads writing to the same result buffer leads to inconsistency while directly using locks and atomic operations lead to large synchronization overheads. We develop a memory pool with thread-safe data structures on GPUs to handle such difficulties. Third, maintaining the sequence information among words is essential for lossless compression. We design a sequence-support strategy, which maintains high GPU parallelism while ensuring sequence information.Our experimental evaluations show that G-TADOC provides 31.1 x average speedup compared to state-of-the-art TADOC.
Year
DOI
Venue
2021
10.1109/ICDE51399.2021.00148
2021 IEEE 37TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2021)
Keywords
DocType
ISSN
TADOC, GPU, parallelism, analytics on compressed data
Conference
1084-4627
Citations 
PageRank 
References 
1
0.35
0
Authors
7
Name
Order
Citations
PageRank
Feng Zhang17914.36
Zaifeng Pan210.35
Yanliang Zhou310.35
Jidong Zhai434036.27
Xipeng Shen52025118.55
Onur Mutlu69446357.40
Xiaoyong Du7882123.29