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- research-articleJuly 2015
Mega-KV: a case for GPUs to maximize the throughput of in-memory key-value stores
Proceedings of the VLDB Endowment (PVLDB), Volume 8, Issue 11Pages 1226–1237https://doi.org/10.14778/2809974.2809984In-memory key-value stores play a critical role in data processing to provide high throughput and low latency data accesses. In-memory key-value stores have several unique properties that include (1) data intensive operations demanding high memory ...
- research-articleMay 2015
Work-efficient parallel skyline computation for the GPU
Proceedings of the VLDB Endowment (PVLDB), Volume 8, Issue 9Pages 962–973https://doi.org/10.14778/2777598.2777605The skyline operator returns records in a dataset that provide optimal trade-offs of multiple dimensions. State-of-the-art skyline computation involves complex tree traversals, data-ordering, and conditional branching to minimize the number of point-to-...
- research-articleMay 2011
Efficient parallel lists intersection and index compression algorithms using graphics processing units
Proceedings of the VLDB Endowment (PVLDB), Volume 4, Issue 8Pages 470–481https://doi.org/10.14778/2002974.2002975Major web search engines answer thousands of queries per second requesting information about billions of web pages. The data sizes and query loads are growing at an exponential rate. To manage the heavy workload, we consider techniques for utilizing a ...
- research-articleFebruary 2011
High-throughput transaction executions on graphics processors
Proceedings of the VLDB Endowment (PVLDB), Volume 4, Issue 5Pages 314–325https://doi.org/10.14778/1952376.1952381OLTP (On-Line Transaction Processing) is an important business system sector in various traditional and emerging online services. Due to the increasing number of users, OLTP systems require high throughput for executing tens of thousands of transactions ...
- research-articleJanuary 2011
Fast sparse matrix-vector multiplication on GPUs: implications for graph mining
Proceedings of the VLDB Endowment (PVLDB), Volume 4, Issue 4Pages 231–242https://doi.org/10.14778/1938545.1938548Scaling up the sparse matrix-vector multiplication kernel on modern Graphics Processing Units (GPU) has been at the heart of numerous studies in both academia and industry. In this article we present a novel non-parametric, self-tunable, approach to data ...