Wu Lisa K. author Columbia University. Computer Science Barker Raymond John author Columbia University. Electrical Engineering Kim Martha Allen author Columbia University. Computer Science Ross Kenneth A. author Columbia University. Computer Science Columbia University. Computer Science originator contributor text Technical reports New York Department of Computer Science, Columbia University 2012 With global pool of data growing at over 2.5 quinitillion bytes per day and over 90% of all data in existence created in the last two years alone, there can be little doubt that we have entered the big data era. This trend has brought database performance to the forefront of high throughput, low energy system design. This paper explores targeted deployment of hardware accelerators to improve the throughput and efficiency of database processing. Partitioning, a critical operation when manipulating large data sets, is often the limiting factor in database performance, and represents a significant amount of the overall runtime of database processing workloads. This paper describes a hardware-software streaming framework and a hardware accelerator for range partitioning, or HARP. The streaming framework offers seamless execution environment for database processing elements such as HARP. HARP offers performance, as well as orders of magnitude gains in power and area efficiency. A detailed analysis of a 32nm physical design shows 9.3 times the throughput of a highly optimized and optimistic software implementation, while consuming just 3.6% of the area and 2.6% of the power of a single Xeon core in the same technology generation. Computer science CUCS-014-12 http://hdl.handle.net/10022/AC:P:14724 English NNC NNC 2012-09-20 12:39:34 -0400 2012-09-20 13:01:04 -0400 8745 eng