Memory performance of and-parallel prolog on shared-memory architectures

The goal of the RAP-WAM AND-parallel Prolog abstract architecture is to provide inference speeds significantly beyond those of sequential systems, while supporting Prolog semantics and preserving sequential performance and storage efficiency. This paper presents simulation results supporting these claims with special emphasis on memory performance on a two-level sharedmemory multiprocessor organization. Several solutions to the cache coherency problem are analyzed. It is shown that RAP-WAM offers good locality and storage efficiency and that it can effectively take advantage of broadcast caches. It is argued that speeds in excess of 2 ML IPS on real applications exhibiting medium parallelism can be attained with current technology

CROSS-LAYER CUSTOMIZATION FOR LOW POWER AND HIGH PERFORMANCE EMBEDDED MULTI-CORE PROCESSORS

Due to physical limitations and design difficulties, computer processor architecture has shifted to multi-core and even many-core based approaches in recent years. Such architectures provide potentials for sustainable performance scaling into future peta-scale/exa-scale computing platforms, at affordable power budget, design complexity, and verification efforts. To date, multi-core processor products have been replacing uni-core processors in almost every market segment, including embedded systems, general-purpose desktops and laptops, and super computers. However, many issues still remain with multi-core processor architectures that need to be addressed before their potentials could be fully realized. People in both academia and industry research community are still seeking proper ways to make efficient and effective use of these processors. The issues involve hardware architecture trade-offs, the system software service, the run-time management, and user application design, which demand more research effort into this field. Due to the architectural specialties with multi-core based computers, a Cross-Layer Customization framework is proposed in this work, which combines application specific information and system platform features, along with necessary operating system service support, to achieve exceptional power and performance efficiency for targeted multi-core platforms. Several topics are covered with specific optimization goals, including snoop cache coherence protocol, inter-core communication for producer-consumer applications, synchronization mechanisms, and off-chip memory bandwidth limitations. Analysis of benchmark program execution with conventional mechanisms is made to reveal the overheads in terms of power and performance. Specific customizations are proposed to eliminate such overheads with support from hardware, system software, compiler, and user applications. Experiments show significant improvement on system performance and power efficiency

Automatic Sharing Classification and Timely Push for Cache-coherent Systems

This paper proposes and evaluates Sharing/Timing Adaptive Push (STAP), a dynamic scheme for preemptively sending data from producers to consumers to minimize criticalpath communication latency. STAP uses small hardware buffers to dynamically detect sharing patterns and timing requirements. The scheme applies to both intra-node and inter-socket directorybased shared memory networks. We integrate STAP into a MOESI cache-coherence protocol using heuristics to detect different data sharing patterns, including broadcasts, producer/consumer, and migratory-data sharing. Using 12 benchmarks from the PARSEC and SPLASH-2 suites in 3 different configurations, we show that our scheme significantly reduces communication latency in NUMA systems and achieves an average of 10% performance improvement (up to 46%), with at most 2% on-chip storage overhead. When combined with existing prefetch schemes, STAP either outperforms prefetching or combines with prefetching for improved performance (up to 15% extra) in most cases

Reducing consistency traffic and cache misses in the avalanche multiprocessor

Journal ArticleFor a parallel architecture to scale effectively, communication latency between processors must be avoided. We have found that the source of a large number of avoidable cache misses is the use of hardwired write-invalidate coherency protocols, which often exhibit high cache miss rates due to excessive invalidations and subsequent reloading of shared data. In the Avalanche project at the University of Utah, we are building a 64-node multiprocessor designed to reduce the end-to-end communication latency of both shared memory and message passing programs. As part of our design efforts, we are evaluating the potential performance benefits and implementation complexity of providing hardware support for multiple coherency protocols. Using a detailed architecture simulation of Avalanche, we have found that support for multiple consistency protocols can reduce the time parallel applications spend stalled on memory operations by up to 66% and overall execution time by up to 31%. Most of this reduction in memory stall time is due to a novel release-consistent multiple-writer write-update protocol implemented using a write state buffer