This paper focuses on running scans in a main memory data processing system at “bare metal” speed. Essentially, this means that the system must aim to process data at or near the speed of the processor (the fastest component in most system configurations). Scans are common in main memory data processing environments, and with the state-of-the-art techniques it still takes many cycles per input tuple to apply simple predicates on a single column of a table. In this paper, we propose a technique called BitWeaving that exploits the parallelism available at the bit level in modern processors. BitWeaving operates on multiple bits of data in a single cycle, processing bits from different columns in each cycle. Thus, bits from a batch of tuples are processed in each cycle, allowing BitWeaving to drop the cycles per column to below one in some case. BitWeaving comes in two flavors: BitWeaving\/V which looks like a columnar organization but at the bit level, and BitWeaving\/H which packs bits horizontally. In this paper we also develop the arithmetic framework that is needed to evaluate predicates using these BitWeaving organizations. Our experimental results show that both these methods produce significant performance benefits over the existing state-of-the-art methods, and in some cases produce over an order of magnitude in performance improvement.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper focuses on running scans in a main memory data processing system at “bare metal” speed. Essentially, this means that the system must aim to process data at or near the speed of the processor (the fastest component in most system configurations). 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