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| Content Provider | ACM Digital Library |
|---|---|
| Author | Kim, Martha A. Wu, Lisa Barker, Raymond J. Ross, Kenneth A. |
| Abstract | The global pool of data is growing at 2.5 quintillion bytes per day, with 90% of it produced in the last two years alone [24]. There is no doubt the era of big data has arrived. This paper explores targeted deployment of hardware accelerators to improve the throughput and energy efficiency of large-scale data processing. In particular, data partitioning is a critical operation for manipulating large data sets. It is often the limiting factor in database performance and represents a significant fraction of the overall runtime of large data queries. To accelerate partitioning, this paper describes a hardware accelerator for range partitioning, or HARP, and a hardware-software data streaming framework. The streaming framework offers a seamless execution environment for streaming accelerators such as HARP. Together, HARP and the streaming framework provide an order of magnitude improvement in partitioning performance and energy. A detailed analysis of a 32nm physical design shows 7.8 times the throughput of a highly optimized and optimistic software implementation, while consuming just 6.9% of the area and 4.3% of the power of a single Xeon core in the same technology generation. |
| Starting Page | 249 |
| Ending Page | 260 |
| Page Count | 12 |
| File Format | |
| ISSN | 01635964 |
| DOI | 10.1145/2508148.2485944 |
| Journal | ACM SIGARCH Computer Architecture News (CARN) |
| Volume Number | 41 |
| Issue Number | 3 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 1981-04-01 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Data partitioning Microarchitecture Streaming data Specialized functional unit Accelerator |
| Content Type | Text |
| Resource Type | Article |
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