1999Unpublished venueRequires access

Memory system support for image processing

Lixin Zhang, John B. Carter, Wilson C. Hsieh, Sally A. McKee

Open publisher page 13 citations

Abstract

Image processing applications tend to access their data non-sequentially and reuse that data infrequently. As a result, they tend to perform poorly on conventional memory systems due to high cache and TLB miss rates and are particularly sensitive to the growing latency of main memory. In this paper, we analyze the memory performance of three image processing algorithms (volume rendering, image warping, andimage filtering) on both a conventional memory system and on the Impulse memory system. The Impulse memory system allows application software to control how, when, and where data are loaded into a conventional processor cache. It does this by letting software configure how the memory controller interprets the physical addresses exported by the processor, which enables an application to dynamically change how data are fetched. Sparse data can be accessed densely, which improves both cache and TLB utilization, and memory latency is hidden by prefetching data within the memory controller. We find that for these image processing codes, using an Impulse memory system yields speedups of 40 % to 226 % over an otherwise identical machine with a conventional memory system. 1.

About this research paper

What this paper is about

Image processing applications tend to access their data non-sequentially and reuse that data infrequently. As a result, they tend to perform poorly on conventional memory systems due to high cache and TLB miss rates and are particularly sensitive to the growing latency of main memory. In this paper, we analyze the memory performance of three image processing algorithms (volume rendering, image warping, andimage filtering) on both a conventional memory system and on the Impulse memory system. The Impulse memory system allows application software to control how, when, and where data are loaded into a conventional processor cache. It does this by letting software configure how the memory controller interprets the physical addresses exported by the processor, which enables an application to dynamically change how data are fetched. Sparse data can be accessed densely, which improves both cache and TLB utilization, and memory latency is hidden by prefetching data within the memory controller. We find that for these image processing codes, using an Impulse memory system yields speedups of 40 % to 226 % over an otherwise identical machine with a conventional memory system. 1.

Why it matters

OpenAlex reports 13 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

Image processing applications tend to access their data non-sequentially and reuse that data infrequently. As a result, they tend to perform poorly on conventional memory systems due to high cache and TLB miss rates and are particularly sensitive to the growing latency of main memory. In this paper, we analyze the memory performance of three image processing algorithms (volume rendering, image warping, andimage filtering) on both a conventional memory system and on the Impulse memory system. The Impulse memory system allows application software to control how, when, and where data are loaded into a conventional processor cache. It does this by letting software configure how the memory controller interprets the physical addresses exported by the processor, which enables an application to dynamically change how data are fetched. Sparse data can be accessed densely, which improves both cache and TLB utilization, and memory latency is hidden by prefetching data within the memory controller. We find that for these image processing codes, using an Impulse memory system yields speedups of 40 % to 226 % over an otherwise identical machine with a conventional memory system. 1.

Key concepts: Computer science, Interleaved memory, Registered memory, Uniform memory access, Memory map, Memory controller, Flat memory model, Virtual memory

Related papers

Back to paper searchBrowse research topicsOriginal source
Memory system support for image processing — Research Paper | ScholarLens