(D) Die nachfolgende Liste ist nicht vollständig. Es gibt immer studentische Arbeiten in verschiedene Forschungsprojekte bei uns am Lehrstuhl. Bitte bei Interesse uns eine Email schreiben (z.B. an Dai Yang oder PD Weidendorfer).

(GB) The following list is not exhaustive. There is always some student work to be done in various research projects. You can send an email (e.g. to Dai Yang or PD Weidendorfer), asking for currently available topics.

(D) Studenten mit eigenen Ideen (d.h. die Idee/das Projekt ist nicht von/für eine Firma oder Organisation), sind dazu eingeladen, mir (Dai Yang) eine Mail zu schreiben. Eine einseitige Beschreibung mit Angaben über das Problem, den Aufwand, mögliche Schritte und gewünschte Lösung sollte mit beigefügt werden. Falls die Idee interessant und gut durchdacht ist, können wir sie in eine Bachelorarbeit verwandeln. Bitte beachte: diese Regelung gilt nicht für Masterarbeiten. 

(GB) Students with OWN Ideas (this means the project MUST NOT be done with/be related to any company, other organisations, etc.), are encouraged to drop me (Dai Yang) a single page proposal stating clearly the problem, its effort, the potential steps and the desired outcome for a discussion. Proposals in a good quality may be granted as bachelor's thesis. This rule does not apply for master's thesis.

 

 

 

Implementation and Evaluation of MLEM algorithm on Intel Xeon Phi Knights Landing (KNL) Processor

In a current project the Chair for Computer Architecture analyzes modern HPC system with heterogeneous architectures towards exascale computing. Real-world applications which represent a class of typical HPC problems are an important element. One example is the maximum likelihood expectation maximization (MLEM) algorithm [KWS+09], which is used for image reconstruction in positron emission tomography (PET). PET visualizes functional processes by measuring the distribution of a tracer of radioisotopes injected into a subjects’s body. Clinical PET scanners for example assist in tumor diagnosis. PET research currently focuses on improving spatial resolution and sensitivity of the technique. Our research is done on small animal PET scanners for preclinical stuies in cooperation with the Medical Institute Rechts der Isar (MRI). The MLEM algorithm is based on sparse matrix vector multiplication (SpMV). The efficient usage of heterogeneous systems with accelerator cards such as Intel Xeon Phi is still an open challenge. We have already developed an efficient implementation for MLEM on multicore architectures. In this work we seek for an efficient implementation of the MLEM algorithm on Xeon Phi (Knight’s landing) using hight-bandwidth memory (HBM). Verification is to be done by benchmarking against the Intel Math Kernel Library (MKL). A cluster system consisting of Xeon Phis is available at LRZ (CooLMUC3).

Contact: Tilman Küstner

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