1991Unpublished venueRequires access

Two Techniques to Enhance the Performance of Memory Consistency Models.

Kourosh Gharachorloo, Anoop Gupta, John L. Hennessy

Open publisher page 275 citations

Abstract

The memory consistency model supported by a multiprocessor directly affects its performance. Thus, several attempts have been made to relax the consistency models to allow for more buffering and pipelining of memory accesses. Unfortunately, the potential increase in performance afforded by relaxing the consistency model is accompanied by a more complex programming model. This paper introduces two general implementation techniques that provide higher performance for all the models. The first technique involves prefetching values for accesses that are delayed due to consistency model constraints. The second technique employs speculative execution to allow the processor to proceed even though the consistency model requires the memory accesses to be delayed. When combined, the above techniques alleviate the limitations imposed by a consistency model on buffering and pipelining of memory accesses, thus significantly reducing the impact of the memory consistency model on performance. 1 Intro...

About this research paper

What this paper is about

The memory consistency model supported by a multiprocessor directly affects its performance. Thus, several attempts have been made to relax the consistency models to allow for more buffering and pipelining of memory accesses. Unfortunately, the potential increase in performance afforded by relaxing the consistency model is accompanied by a more complex programming model. This paper introduces two general implementation techniques that provide higher performance for all the models. The first technique involves prefetching values for accesses that are delayed due to consistency model constraints. The second technique employs speculative execution to allow the processor to proceed even though the consistency model requires the memory accesses to be delayed. When combined, the above techniques alleviate the limitations imposed by a consistency model on buffering and pipelining of memory accesses, thus significantly reducing the impact of the memory consistency model on performance. 1 Intro...

Why it matters

OpenAlex reports 275 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

The memory consistency model supported by a multiprocessor directly affects its performance. Thus, several attempts have been made to relax the consistency models to allow for more buffering and pipelining of memory accesses. Unfortunately, the potential increase in performance afforded by relaxing the consistency model is accompanied by a more complex programming model. This paper introduces two general implementation techniques that provide higher performance for all the models. The first technique involves prefetching values for accesses that are delayed due to consistency model constraints. The second technique employs speculative execution to allow the processor to proceed even though the consistency model requires the memory accesses to be delayed. When combined, the above techniques alleviate the limitations imposed by a consistency model on buffering and pipelining of memory accesses, thus significantly reducing the impact of the memory consistency model on performance. 1 Intro...

Key concepts: Consistency model, Sequential consistency, Computer science, Consistency (knowledge bases), Parallel computing, Cache coherence, Memory model, Weak consistency

Related papers

Back to paper searchBrowse research topicsOriginal source
Two Techniques to Enhance the Performance of Memory Consistency Models. — Research Paper | ScholarLens