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Performance Analysis of kNN Query Processing on large datasets using CUDA & Pthreads: comparing between CPU & GPU
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
2017 (engelsk)Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
Abstract [en]

Telecom companies do a lot of analytics to provide consumers a better service and to stay in competition. These companies accumulate special big data that has potential to provide inputs for business. Query processing is one of the major tool to fire analytics at their data.

Traditional query processing techniques which follow in-memory algorithm cannot cope up with the large amount of data of telecom operators. The k nearest neighbour technique(kNN) is best suitable method for classification and regression of large datasets. Our research is focussed on implementation of kNN as query processing algorithm and evaluate the performance of it on large datasets using single core, multi-core and on GPU.

This thesis shows an experimental implementation of kNN query processing on single core CPU, Multicore CPU and GPU using Python, P- threads and CUDA respectively. We considered different levels of sizes, dimensions and k as inputs to evaluate the performance. The experiment shows that GPU performs better than CPU single core on the order of 1.4 to 3 times and CPU multi-core on the order of 5.8 to 16 times for different levels of inputs.

sted, utgiver, år, opplag, sider
2017. , s. 60
Emneord [en]
GPU, Multicore CPU, Parallel computing, Performance, Single core CPU, kNN, Query Processing
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-14758OAI: oai:DiVA.org:bth-14758DiVA, id: diva2:1120683
Fag / kurs
ET2580 Master's Thesis (120 credits) in Electrical Engineering with emphasis on Telecommunication Systems
Utdanningsprogram
ETATX Master of Science Programme in Electrical Engineering with emphasis on Telecommunication Systems
Tilgjengelig fra: 2017-07-07 Laget: 2017-07-06 Sist oppdatert: 2017-07-07bibliografisk kontrollert

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