Although some instructions hurt performance more than others, current processors typically apply scheduling and speculation as if each instruction was equally costly. Instruction cost can be naturally expressed through the critical path: if we could predict it at run-time, egalitarian policies could be replaced with cost-sensitive strategies that will grow increasingly effective as processors become more parallel.This paper introduces a hardware predictor of instruction criticality and uses it to improve performance. The predictor is both effective and simple in its hardware implementation. The effectiveness at improving performance stems from using a dependence-graph model of the microarchitectural critical path that identifies execution bottlenecks by incorporating both data and machine-specific dependences. The simplicity stems from a token-passing algorithm that computes the critical path without actually building the dependence graph.By focusing processor policies on critical instructions, our predictor enables a large class of optimizations. It can (i) give priority to critical instructions for scarce resources (functional units, ports, predictor entries); and (ii) suppress speculation on non-critical instructions, thus reducing "useless" misspeculations. We present two case studies that illustrate the potential of the two types of optimization, we show that (i) critical-pathbased dynamic instruction scheduling and steering in a clustered architecture improves performance by as much as 21% (10% on average); and (ii) focusing value prediction only on critical instructions improves performance by as much as 5%, due to removing nearly half of the misspeculations.

Abstract

Abstract. Recursive data structures (lists, trees, graphs, etc.) are used throughout scientific and commercial software. The common approach is to allocate storage to the individual nodes of such structures dynamically, maintaining the logical connection between them via pointers. Once such a data structure goes through a sequence of updates (inserts and deletes), it may get scattered all over memory yielding poor spatial locality, which in turn introduces many cache misses. In this paper we present the new concept of Virtual Cache Lines (VCLs). Basically, the mechanism keeps groups of consecutive nodes in close proximity, forming virtual cache lines, while allowing the groups to be stored arbitrarily far away from each other. Virtual cache lines increase the spatial locality of the given data structure resulting in better locality of references. Furthermore, since the spatial locality is improved, software prefetching becomes much more attractive. Indeed, we also present a software prefetching algorithm that can be used when dealing with VCLs resulting in even higher data cache performance. Our results show that the average performance of linked list operations, like scan, insert, and delete can be improved by more than 200% even in architectures that do not support prefetching, like the Intel Pentium. Moreover, when using prefetching one can gain additional 100% improvement. We believe that given a program that manipulates certain recursive data structures, compilers will be able to generate VCL-based code. Also, until this vision becomes true, VCLs can be used to build more efficient user libraries, operating-systems and applications programs.

Data-layout optimizations rearrange fields within objects, objects within objects, and objects within the heap, with the goal of increasing spatial locality. While the importance of data-layout optimizations has been growing, their deployment has been limited, partly because they lack a unifying framework. We propose a parameterizable framework for data-layout optimization of generalpurpose applications. Acknowledging that finding an optimal layout is not only NP-hard, but also poorly approximable, our framework finds a good layout by searching the space of possible layouts, with the help of profile feedback. The search process iteratively prototypes candidate data layouts, evaluating them by "simulating" the program on a representative trace of memory accesses. To make the search process practical, we develop space-reduction heuristics and optimize the expensive simulation via memoization. Equipped with this iterative approach, we can synthesize layouts that outperform existing non-iterative heuristics, tune application-specific memory allocators, as well as compose multiple data-layout optimizations.

Existing reputation systems used by online auction houses do not address the concern of a buyer shopping for commodities-finding a good bargain. These systems do not provide information on the practices adopted by sellers to ensure profitable auctions. These practices may be legitimate, like imposing a minimum starting bid on an auction, or fraudulent, like using colluding bidders to inflate the final price in a practice known as shilling.We develop a reputation system to help buyers identify sellers whose auctions seem price-inflated. Our reputation system is based upon models that characterize sellers according to statistical metrics related to price inflation. We combine the statistical models with anomaly detection techniques to identify the set of suspicious sellers. The output of our reputation system is a set of values for each seller representing the confidence with which the system can say that the auctions of the seller are price-inflated.We evaluate our reputation system on 604 high-volume sellers who posted 37,525 auctions on eBay. Our system automatically pinpoints sellers whose auctions contain potential shill bidders. When we manually analyze these sellers' auctions, we find that many winning bids are at about the items' market values, thus undercutting a buyer's ability to find a bargain and demonstrating the effectiveness of our reputation system.