We consider the fundamental operation of applying a compound filtering condition to a set of records. With large main memories available cheaply, systems may choose to keep the data entirely in main memory, in order to improve query and/or update performance. The design of a data-intensive algorithm in main memory needs to take into account the architectural characteristics of modern processors, just as a disk-based method needs to consider the physical characteristics of disk devices. An important architectural feature that influences the performance of main memory algorithms is the branch misprediction penalty. We demonstrate that branch misprediction has a substantial impact on the performance of an algorithm for applying selection conditions. We describe a space of “query plans ” that are logically equivalent, but differ in terms of performance due to variations in their branch prediction behavior. We propose a cost model that takes branch prediction into account, and develop a query optimization algorithm that chooses a plan with optimal estimated cost for conjunctive conditions. We also develop an efficient heuristic optimization algorithm. We also show how records can be ordered to further reduce branch misprediction effects.

Multimedia applications are fast becoming one of the domi-nating workloads for modern computer systems. Since these applications normally have large data sets and little data-reuse, many researchers believe that they have poor memory behavior compared to traditional programs, and that current cache architectures cannot handle them well. It is there-fore important to quantitatively characterize the memory be-havior of these applications in order to provide insights for future design and research of memory systems. However, very few results on this topic have been published. This pa-per presents a comprehensive research on the memory re-quirements of a group of programs that are representative of multimedia applications. These programs include a sub-set of the popular MediaBench suite and several large mul-timedia programs running on the Linux, Windows NT and Tru UNIX operating systems. We performed extensive mea-surement and trace-driven simulation experiments. We then compared the memory utilization of these programs to that of SPECint95 applications. We found that multimedia applica-tions actually have better memory behavior than SPECint95 programs. The high cache hit rates of multimedia applica-tions can be contributed to the following three factors. Most multimedia applications apply block partitioning algorithms to the input data, and work on small blocks of data that eas-ily fit into the cache. Secondly, within these blocks, there is significant data reuse as well as spatial locality. The third reason is that a large number of references generated by multimedia applications are to their internal data struc-tures, which are relatively small and can also easily fit into reasonably-sized caches. 1.

On-chip performance counters are gaining popularity as an analysis and validation tool. Most contemporary processors have between two and six physical counters that can monitor an equal number of unique events simultaneously at fixed sampling periods. Through multiplexing and estimation, an even greater number of unique events can be monitored in a single program execution. When a program is sampled in multiplexed mode using round-robin scheduling of a specified event set, the number of events that are physically counted during each sampling period is limited by the number of counters that can be simultaneously accessed. During this period, the remaining events of the multiplexed event-set are not monitored, but their counts are estimated. Our work quantifies the estimation error of the event-counts in the multiplexed mode, which indicates that as many as 42 % of sampled intervals are estimated with error greater than 10%. We propose new estimation algorithms that result in an accuracy improvement of up to 40%. 1

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On-chip performance counters are gaining popularity as an analysis and validation tool. Various drivers and interfaces have been developed to access these counters. Most contemporary processors have between two and six physical counters that can monitor an equal number of unique events simultaneously at fixed sampling periods. Through multiplexing and estimation, an even greater number of unique events can be monitored using round-robin scheduling of event sets. When program execution is sampled in multiplexed mode, the counters are interfaced to a subset of events (limited by the number of physical counters) and are incremented appropriately. At this sampling slice, the remaining events in the set do not access the counters, but the respective counts of these events are estimated. Our work addresses the error associated with the estimation of event counts during multiplexed mode. We quantify this error and propose new estimation algorithms that result in much improved accuracy.

We consider the fundamental operation of applying a conjunction of selection conditions to a set of records. With large main memories available cheaply, systems may choose to keep the data entirely in main memory, in order to improve query and/or update performance.

I dedicate this work to my parents. iii ACKNOWLEDGEMENTS First and foremost, I thank my advisor Dr. Jeanine Cook for guiding me through the path to success; I also thank her for all the encouragement, resources and support. I take this opportunity to express my gratitude to my committee members, Dr. Juris Reinfelds and Dr. Richard Oliver, who took time off from their tight schedule and provided me with their invaluable suggestions. I thank Dr. Steven Stochaj for providing us with a stand alone machine; my research would not have been possible without it. I am also grateful to Dr. Eric Johnson who guided me through the initial stages of MS and whose teaching is impeccable. I express my heartfelt gratitude to my parents, who have made my dreams come alive. Their immense love, care and support has been a perennial source of encouragement and inspiration. I thank my mom, for her prayers and her emotional and mental support; my dad, for endowing me with all the good things in life. My friends play a very crucial role in what I am today. My special thanks to: