Practical Structure Layout Optimization and Advice

Hundt, Robert; Mannarswamy, Sandya; Chakrabarti, Dhruva R.

doi:10.1109/cgo.2006.29

Cited by 32 publications

(19 citation statements)

References 17 publications

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“…If one of the following conditions is true for a structure type, ASLOP stops performing data-layout transformations on it. This restriction is comparable with the one used in [5].…”

Section: Safety Checkmentioning

confidence: 74%

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ASLOP: A field-access affinity-based structure data layout optimizer

Yan

Chen

et al. 2011

Sci. China Inf. Sci.

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By rearranging the data, data layout optimizations improve the utilization of a cache line between two of its successive refills, thus reducing the total number of cache line refills and improving the performance of a program. In this paper, we show that to enable structure data layout optimizations to be effective, two parameters, namely intra-instance affinity and inter-instance affinity, need to be considered at the same time in order to model the cache line utilization more accurately. We also propose a lightweight approach to measure intra-instance affinity and inter-instance affinity to avoid complex memory trace analyses. A prototype, called ASLOP, has been implemented in the Open64 compiler and evaluated using benchmarks from SPEC CPU 2000, SPEC CPU 2006 and Olden benchmark suites that have extensive structure types. Our approach can achieve up to 48.1% performance improvement over the original programs, and 11.9% over the optimized programs using maximal reshaping, an existing approach that is known to produce close to the best results, on the two platforms we tested.

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“…If one of the following conditions is true for a structure type, ASLOP stops performing data-layout transformations on it. This restriction is comparable with the one used in [5].…”

Section: Safety Checkmentioning

confidence: 74%

“…This twoportion scheme restricts layout strategies that can be applied, thus it loses certain layout opportunities. Hundt [5] also proposes structure peeling, which is similar to maximal reshaping discussed earlier.…”

Section: Related Workmentioning

confidence: 99%

ASLOP: A field-access affinity-based structure data layout optimizer

Yan

Chen

et al. 2011

Sci. China Inf. Sci.

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“…The work in [5] presents a framework for structure layout optimizations, which include structure splitting and structure peeling, and incorporates dead field removal and field reordering as part of the layout process. The work in [3] describes in detail the structure splitting and structure peeling optimizations in the Open64 Compiler.…”

Section: Data Layout Optimizationmentioning

confidence: 99%

“…. , A [8], and the most frequent access pattern in the entire program is groups of [3], A [6], followed by groups of A [1], A [4], A [7], and then by groups of A [2], A [5], A [8]. Suppose further that each cache line in the system can only hold exactly three array elements; that is, array elements A[0 : 2] reside in the same cache line, and similarly for the elements A [3 : 5] and A [6 : 8].…”

Section: Array Re-mapping Optimizationmentioning

confidence: 99%

“…Mathematically, alpha(i) = 3 * (i % 3) + (i / 3). In fact, this specific construction of alpha was chosen to achieve the same result as if data layout optimization was performed on the entire nineelement array A to become A[0], A [3], A [6], A [1], A [4], A [7], A [2], A [5], A [8]. In order for the compiler to determine if an array is a good candidate for the re-mapping optimization, it will have to analyze its access patterns.…”

Section: Array Re-mapping Optimizationmentioning

confidence: 99%

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Multi-core scalability-impacting compiler optimizations

Suresh

Lai

2010

Comput Sci Res Dev

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In a multi-core system, while the processor core pipelines and local caches are replicated in each core, other resources, such as shared cache and memory bus, are shared across all cores in a processor. Running multiple copies of memory intensive applications on different cores often leads to poor scaling, because these shared resources can become bottlenecks to throughput performance. Such issues have been traditionally studied under the design and evaluation of processors, platforms, and operating systems. We have identified a set of compiler optimizations that have measurable impact on the scaling of applications on multi-core systems and evaluated them based on the standard rate run of the SPEC CPU2006 benchmark suite, where throughput is measured by running multiple copies of a program in a multi-core and multi-processor system. We have also collected data and analyzed how these compiler optimizations affect the utilization and behaviors of the shared resources. Through our experimental results, we show that conventional compiler optimizations can play an important role in improving the scaling of running memory intensive application threads on multi-core systems.

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