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Rendering and Image Processing for Micro Lithography on Xeon Phi Knights Landing Processor
KTH, School of Electrical Engineering and Computer Science (EECS).
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The Segment program in Mycronics laser mask writers converts vector graphics into raster image with high computation intensity. IntelR Xeon PhiTM Knights Landing (KNL) is a many-core processor delivering massive thread and data parallelism. This project explores whether KNL can be a good candidate as a data processing platform in microlithography applications. The feasibility is studied through profiling the program on KNL together with comparing the performance on KNL with other architectures, including the current platform. Several optimization methods are implemented targeting KNL, resulting in speed-up up to 5%. The cost of the systems is taken into consideration. The high-level parallel application can take the advantage of the huge number of cores, leading to the high performance per cost together with the relatively low price of KNL. Hence, KNL can be a nice replacement for the current platform as a high-performance patterngenerator.

Abstract [sv]

Segmentprogrammet i Mycronics laserskrivare omvandlar vektorgrafik till rasterbild med hög beräkningsintensitet. IntelR Xeon PhiTM Knights Landing (KNL) är en process med många kärnor som levererar omfattande tråd och dataparallellitet. Detta projekt undersöker om KNL kan vara en bra kandidat som databehandlingsplattform i mikrolitografiska applikationer. Genomförbarheten studeras genom att profilera programmet på KNL tillsammans med att jämföra prestanda på KNL med andra arkitekturer, inklusive den nuvarande plattformen. Flera optimeringsmetoder implementeras med inriktning på KNL, vilket resulterar i effektivitetshöjningar upp till 5 %. Kostnaden för systemen beaktas. Den högt parallelliserade applikationen kan dra fördel av det stora antalet kärnor, vilket leder till hög prestanda per kostnad tillsammans med det relativt låga priset på KNL. Därför kan KNL vara en bra ersättare för den nuvarande plattformen som en högpresteran-de mönstergenerator.

Place, publisher, year, edition, pages
2018.
Series
TRITA-EECS-EX ; 2018:756
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-241088OAI: oai:DiVA.org:kth-241088DiVA, id: diva2:1276962
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Examiners
Available from: 2019-01-10 Created: 2019-01-09 Last updated: 2019-01-10Bibliographically approved

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