Sökning: "högpresterande beräkningar"

Visar resultat 6 - 10 av 21 uppsatser innehållade orden högpresterande beräkningar.

1. 6. An I/O-aware scheduler for containerized data-intensive HPC tasks in Kubernetes-based heterogeneous clusters

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Zheyun Wu; [2022]
   Nyckelord :Cloud-native; Containers; Kubernetes; High-performance computing HPC ; Data-intensive computing; Task scheduling; Heterogeneous systems; Cloud-native; Containrar; Kubernetes; Högpresterande datoranvändning HPC ; Dataintensiv datoranvändning; Uppgiftsschemaläggning; Heterogena system;

   Sammanfattning : Cloud-native is a new computing paradigm that takes advantage of key characteristics of cloud computing, where applications are packaged as containers. The lifecycle of containerized applications is typically managed by container orchestration tools such as Kubernetes, the most popular container orchestration system that automates the containers’ deployment, maintenance, and scaling. LÄS MER

3. 7. AXI-PACK : Near-memory Bus Packing for Bandwidth-Efficient Irregular Workloads

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Chi Zhang; [2022]
   Nyckelord :General propose processor; on-chip bus protocol; irregular memory access; ASIC digital circuit design.; Generellt förslag på processor; on-chip-bussprotokoll; oregelbunden minnesåtkomst; digital ASIC-kretsdesign.;

   Sammanfattning : General propose processor (GPP) are demanded high performance in dataintensive applications, such as deep learning, high performance computation (HPC), where algorithm kernels like GEMM (general matrix-matrix multiply) and SPMV (sparse matrix-vector multiply) kernels are intensively used. The performance of these data-intensive applications are bounded with memory bandwidth, which is limited by computing & memory access coupling and memory wall effect. LÄS MER

4. 8. Linkage of Macro- and Micro-scale Modelling Tools for Additive Manufacturing

   Master-uppsats, KTH/Materialvetenskap

   Författare :Julia Sjöström; [2020]
   Nyckelord :Maraging steel; selective laser melting; temperature evolution; macro-scale modelling; segregation; ICME.;

   Sammanfattning : Additive manufacturing methods for steel are competing against commercial production in an increasing pace. The geometry freedom together with the high strength and toughness due to extreme cooling rates make this method viable to use for high-performance components. The desirable material properties originate from the ultrafine grain structures. LÄS MER

5. 9. Evaluation of methods for quantifying returns within the premium pension

   Master-uppsats, KTH/Matematisk statistik

   Författare :Emil Backman; David Petersson; [2020]
   Nyckelord :Pension; internal rate of return; applied mathematics; big matrix; numerical methods; large eigenvalue problem; finance; risk analysis; extreme value modeling; probability; stochastic modeling; Pension; intern ränta; tillämpad matematik; stora matrismetoder; numeriska metoder; stora egenvärdesproblem; finans; riskanalys; modellering av extrema värden; sannolikhet; stokastisk modellering;

   Sammanfattning : Pensionsmyndigheten's (the Swedish Pensions Agency) current calculation of the internal rate of return for 7.7 million premium pension savers is both time and resource consuming. LÄS MER

7. 10. Revision of an artificial neural network enabling industrial sorting

   Master-uppsats, Uppsala universitet/Institutionen för teknikvetenskaper

   Författare :Henrik Malmgren; [2019]
   Nyckelord :artificial neural networks; machine learning; deep learning; connectionism; pattern recognition; machine learning; automation; image analysis; information technology; applied mathematics; mathematical optimization; information theory; mathematical statistics; mathematical models; stochastic models; probabilities; chance; approximations; algorithms; computer programs; computer software; signal processing; high performance computing; numerical methods; high technology industries; sustainable development; artificiella neurala nätverk; maskininlärning; djup maskininlärning; konnektionism; mönsterigenkänning; automatisering; bildanalys; informationsteknik; tillämpad matematik; optimering; informationsteori; statistisk inferens; matematiska modeller; stokastiska modeller; sannolikhetskalkyl; slumpen; approximationer; algoritmer; datorprogram; programvara; signalbehandling; högpresterande beräkningar; numeriska metoder; teknikutveckling; maskinindustri; högteknologisk industri; maskinhandel; skrothandel; bärkraftig utveckling;

   Sammanfattning : Convolutional artificial neural networks can be applied for image-based object classification to inform automated actions, such as handling of objects on a production line. The present thesis describes theoretical background for creating a classifier and explores the effects of introducing a set of relatively recent techniques to an existing ensemble of classifiers in use for an industrial sorting system. LÄS MER