Parallel systems that support the shared memory abstraction are becoming widely accepted in many areas of computing. Writing correct and efficient programs for such systems requires a formal specification of memory semantics, called a memory consistency model. The most intuitive model-sequential consistency-greatly restricts the use of many performance optimizations commonly used by uniprocessor hardware and compiler designers, thereby reducing the benefit of using a multiprocessor. To alleviate this problem, many current multiprocessors support more relaxed consistency models. Unfortunately, the models supported by various systems differ from each other in subtle yet important ways. Furthermore, precisely defining the semantics of each model often leads to complex specifications that are difficult to understand for typical users and builders of computer systems. The purpose of this tutorial paper is to describe issues related to memory consistency models in a way that would be understandable to most computer professionals. We focus on consistency models proposed for hardware-based shared-memory systems. Many of these models are originally specified with an emphasis on the system optimizations they allow. We retain the system-centric emphasis, but use uniform and simple terminology to describe the different models. We also briefly discuss an alternate programmer-centric view that describes the models in terms of program behavior rather than specific system optimizations. 1

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Commercial applications such as databases and Web servers constitute the largest and fastest-growing segment of the market for multiprocessor servers. Ongoing innovations in disk subsystems, along with the ever increasing gap between processor and memory speeds, have elevated memory system design as the critical performance factor for such workloads. However, most current server designs have been optimized to perform well on scientific and engineering workloads, potentially leading to design decisions that are non-ideal for commercial applications. The above problem is exacerbated by the lack of information on the performance requirements of commercial workloads, the lack of available applications for widespread study, and the fact that most representative applications are too large and complex to serve as suitable benchmarks for evaluating trade-offs in the design of processors and servers. This paper presents a detailed performance study of three important classes of commercial workloads: online transaction processing (OLTP), decision support systems (DSS), and Web index search. We use the Oracle commercial database engine for our OLTP and DSS workloads, and the AltaVista search engine for our Web index search workload. This study characterizes the memory system behavior of these workloads through a large number of architectural experiments on Alpha multiprocessors augmented with full system simulations to determine the impact of architectural trends. We also identify a set of simplifications that make these workloads more amenable to monitoring and simulation without affecting representative memory system behavior. We observe that systems optimized for OLTP versus DSS and index search workloads may lead to diverging designs, specifically in the size and speed requirements for off-chip caches.

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