????????? · Scholarworks@UNIST (Ulsan National Institute of Science and Technology) · 2019
??? ????????? ?????? ????????? ???????????? SIMD MAC ??????, ??? ???????????? ??? SIMD MAC ????????? ????????? ????????? ???????????? ????????? ???????????? ?????? ?????????, ????????? ??????????????? ???????????? ?????????????????? ?????? ????????? ???????????? ???????????? ???????????? ????????? DSP???; ??? DSP?????? ????????? ?????? ?????? ???????????? ?????? ?????? ????????? ????????? ?????????????????? ???????????? ??????????????? ?????? ?????????; ?????? ????????? DSP ????????? ????????? ???????????? ???????????? ?????????????????? ?????? ???????????????; ??? ?????? ???????????? ??? ?????????????????? ???????????? ?????? ?????? ?????? ???????????? ???????????? ?????????????????? ????????????.
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Scottish Executive, Office Of The Chief Statistician · Open MIND · 2011
The <i>Scottish Indices of Multiple Deprivation</i> (SIMD) 2004, 2006 and 2009 identify small area concentrations of multiple deprivation across Scotland. Small areas, called datazones, are ranked from 1 (most deprived) to least deprived. The ranking is based on information covering a number of different types or 'domains' of deprivation; income, employment, health, education, access to services, housing and crime (not 2004).<br> <br> Further information is available from the Scottish Government’s <a href="http://www.scotland.gov.uk/Topics/Statistics/SIMD" title="Scottish Index of Multiple Deprivation">SIMD</a> web pages.<br>
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Scottish Government, Office Of The Chief Statistician · Open MIND · 2011
The <i>Scottish Indices of Multiple Deprivation</i> (SIMD) 2004, 2006 and 2009 identify small area concentrations of multiple deprivation across Scotland. Small areas, called datazones, are ranked from 1 (most deprived) to least deprived. The ranking is based on information covering a number of different types or 'domains' of deprivation; income, employment, health, education, access to services, housing and crime (not 2004).<br> <br> Further information is available from the Scottish Government’s <a href="http://www.scotland.gov.uk/Topics/Statistics/SIMD" title="Scottish Index of Multiple Deprivation">SIMD</a> web pages.<br>
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Scottish Executive, Office Of The Chief Statistician · Open MIND · 2011
The <i>Scottish Indices of Multiple Deprivation</i> (SIMD) 2004, 2006 and 2009 identify small area concentrations of multiple deprivation across Scotland. Small areas, called datazones, are ranked from 1 (most deprived) to least deprived. The ranking is based on information covering a number of different types or 'domains' of deprivation; income, employment, health, education, access to services, housing and crime (not 2004).<br> <br> Further information is available from the Scottish Government’s <a href="http://www.scotland.gov.uk/Topics/Statistics/SIMD" title="Scottish Index of Multiple Deprivation">SIMD</a> web pages.<br>
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Health Informatics Centre · Discovery Research Portal (University of Dundee) · 2023
Health Informatic Centre Derived dataset covering Health Board and SIMD.
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Saul I. Gass, Carl M. Harris · 2001
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Urban Big Data Centre · University of Glasgow · 2025
The 694 data zones in Glasgow ranked in 2012, 2009, 2006 and 2004 for overall deprivation. Each data zone also has a local ranking i.e. within Glasgow to ease comparison for that data zone over time and within the city. Intermediate Geographies and political geographies such as the 2011 Scottish Parliamentary Constituencies, multi-member wards are also included.
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Matthias Kretz · Zenodo (CERN European Organization for Nuclear Research) · 2024
First release. It is centered around the GCC/libstdc++ std::experimental::simd implementation and x86.
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Matthias Kretz · Zenodo (CERN European Organization for Nuclear Research) · 2024
First release. It is centered around the GCC/libstdc++ std::experimental::simd implementation and x86.
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Krzikalla, Olaf, Kretz, Matthias · elib (German Aerospace Center) · 2025
One design goal of the simd interface is to enable SIMD-generic code. This is why all arithmetic operators and functions have corresponding overloads. However, arithmetic reductions are still missing overloads for non-simd, vectorizable types.
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Molecular Genetics and Metabolism · 2006
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Kretz, Matthias · Zenodo (CERN European Organization for Nuclear Research) · 2023
This is a bugfix release. What's Changed Fix clang support by @mattkretz in https://github.com/mattkretz/vir-simd/pull/21 Full Changelog: https://github.com/mattkretz/vir-simd/compare/v0.3.0...v0.3.1
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2008
Increasingfocusonmultimediaapplicationshasprompted theadditionofmultimediaextensionstomostexistinggen-eralpurposemicroprocessors.Thisaddedfunctionality comesprimarilywiththeadditionofshortSIMDinstruc-tions.Unfortunately,accesstotheseinstructionsislimited toin-lineassemblyandlibrarycalls.Generally,ithasbeen assumedthatvectorcompilersprovidethemostpromis-ingmeansofexploitingmultimediainstructions.Although vectorizationtechnologyiswellunderstood,itisinherently complexandfragile.Inaddition,itisincapableoflocating
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Kretz, Matthias · GSI Repository (German Federal Government) · 2024
This project aims to provide a fallback std::experimental::simd (Parallelism TS 2) implementation with additional features. Not every user can rely on GCC 11+ and its standard library to be present on all target systems. Therefore, the header vir/simd.h provides a fallback implementation of the TS specification that only implements the scalar and fixed_size<N> ABI tags. Thus, your code can still compile and run correctly, even if it is missing the performance gains a proper implementation provides.What's Changed Fix build with older GCC and Apple Clang. Add vir-simd version https://github.com/mattkretz/vir-simd/pull/27 Check preconditions https://github.com/mattkretz/vir-simd/pull/31 Add assume_matching_size execution policy modifier https://github.com/mattkretz/vir-simd/pull/32 Reduce template bloat in simd fallback https://github.com/mattkretz/vir-simd/pull/33 Fix tuple of complex simdize https://github.com/mattkretz/vir-simd/pull/34 Let simdize be T (instead of ill-formed) if T cannot be simdized. Full Changelog: https://github.com/mattkretz/vir-simd/compare/v0.3.1...v0.3.200
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Manuel Hohenauer, Rainer Leupers · 2009
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Yongjun Park, Sangwon Seo, Hyunchul Park et al. · 2012
Single-instruction multiple-data (SIMD) accelerators provide an energy-efficient platform to scale the performance of mobile systems while still retaining post-programmability. The central challenge is translating the parallel resources of the SIMD hardware into real application performance. In scientific applications, automatic vectorization techniques have proven quite effective at extracting large levels of data-level parallelism (DLP). However, vectorization is often much less effective for media applications due to low trip count loops, complex control flow, and non-uniform execution behavior. As a result, SIMD lanes remain idle due to insufficient DLP. To attack this problem, this paper proposes a new vectorization pass called SIMD Defragmenter to uncover hidden DLP that lurks below the surface in the form of instruction-level parallelism (ILP). The difficulty is managing the data packing/unpacking overhead that can easily exceed the benefits gained through SIMD execution. The SIMD degragmenter overcomes this problem by identifying groups of compatible instructions (subgraphs) that can be executed in parallel across the SIMD lanes. By SIMDizing in bulk at the subgraph level, packing/unpacking overhead is minimized. On a 16-lane SIMD processor, experimental results show that SIMD defragmentation achieves a mean 1.6x speedup over traditional loop vectorization and a 31% gain over prior research approaches for converting ILP to DLP.
Cited by 28
Behrooz Parhami · ACM SIGARCH Computer Architecture News · 1995
I volunteered to write this report during the SIMD panel session held on 2/9/95 at Frontiers '95. All panelists cooperated by sending me their transparency masters. A draft report was prepared based on these transparency masters, position statements published in the Frontiers '95 proceedings on pp. 466-469, and my own notes. The draft was e-mailed on 3/17/95 to the panel organizer/moderator and the panelists for their comments. This final version of the report is based on comments and markups received through 3/31/95. I have drawn from the panelists' ideas freely, using quotation marks only when including their statements verbatim.The panel consisted of both academic and industry experts in the field of massively parallel systems (see the table below). All but Tim Bridges, who is currently involved in a large-scale software development project for the MasPar MP-2 SIMD architecture, have built working SIMD machines. The vast practical experience of the panel was quite evident in the insightful presentations and interactions. It is indeed a privilege for me to have worked on this report.
Cited by 16
Yongjun Park, Sangwon Seo, Hyunchul Park et al. · ACM SIGPLAN Notices · 2012
Single-instruction multiple-data (SIMD) accelerators provide an energy-efficient platform to scale the performance of mobile systems while still retaining post-programmability. The central challenge is translating the parallel resources of the SIMD hardware into real application performance. In scientific applications, automatic vectorization techniques have proven quite effective at extracting large levels of data-level parallelism (DLP). However, vectorization is often much less effective for media applications due to low trip count loops, complex control flow, and non-uniform execution behavior. As a result, SIMD lanes remain idle due to insufficient DLP. To attack this problem, this paper proposes a new vectorization pass called SIMD Defragmenter to uncover hidden DLP that lurks below the surface in the form of instruction-level parallelism (ILP). The difficulty is managing the data packing/unpacking overhead that can easily exceed the benefits gained through SIMD execution. The SIMD degragmenter overcomes this problem by identifying groups of compatible instructions (subgraphs) that can be executed in parallel across the SIMD lanes. By SIMDizing in bulk at the subgraph level, packing/unpacking overhead is minimized. On a 16-lane SIMD processor, experimental results show that SIMD defragmentation achieves a mean 1.6x speedup over traditional loop vectorization and a 31% gain over prior research approaches for converting ILP to DLP.
Cited by 2
Thomas Willhalm, Nicolae Popovici, Yazan Boshmaf et al. · Proceedings of the VLDB Endowment · 2009
The availability of huge system memory, even on standard servers, generated a lot of interest in main memory database engines. In data warehouse systems, highly compressed column-oriented data structures are quite prominent. In order to scale with the data volume and the system load, many of these systems are highly distributed with a shared-nothing approach. The fundamental principle of all systems is a full table scan over one or multiple compressed columns. Recent research proposed different techniques to speedup table scans like intelligent compression or using an additional hardware such as graphic cards or FPGAs. In this paper, we show that utilizing the embedded Vector Processing Units (VPUs) found in standard superscalar processors can speed up the performance of mainmemory full table scan by factors. This is achieved without changing the hardware architecture and thereby without additional power consumption. Moreover, as on-chip VPUs directly access the system's RAM, no additional costly copy operations are needed for using the new SIMD-scan approach in standard main memory database engines. Therefore, we propose this scan approach to be used as the standard scan operator for compressed column-oriented main memory storage. We then discuss how well our solution scales with the number of processor cores; consequently, to what degree it can be applied in multi-threaded environments. To verify the feasibility of our approach, we implemented the proposed techniques on a modern Intel multi-core processor using Intel® Streaming SIMD Extensions (Intel® SSE). In addition, we integrated the new SIMD-scan approach into SAP® Netweaver® Business Warehouse Accelerator. We conclude with describing the performance benefits of using our approach for processing and scanning compressed data using VPUs in column-oriented main memory database systems.
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Kretz, Matthias · Zenodo (CERN European Organization for Nuclear Research) · 2025
What's Changed since 0.4.2 Fix 0.4 simdize for clang Work around Clang bug in simd_permute
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