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SUMMARY:Tackling the Memory Bottlenecks of CPU-GPU Processing of Analytica
 l Workloads
DTSTART:20180601T140000
DTEND:20180601T160000
DTSTAMP:20260407T064247Z
UID:0e9b7dd7cda735df739fe360ea27714e0bbf7050c38332f0e001a918
CATEGORIES:Conferences - Seminars
DESCRIPTION:Panagiotis Sioulas\nEDIC candidacy exam\nExam president: Prof.
  James Larus\nThesis advisor: Prof. Anastasia Ailamaki\nCo-examiner: Prof.
  Christoph Koch\n\nAbstract\nWith the volume of data growing at an exponen
 tial rate\, processing data at a high rate has become all the more critica
 l. However\, even the available processing capabilities of hardware such a
 s multi-core CPUs and GPUs are underutilized in analytical query workloads
  due to the limited bandwidth of interconnects with the main memory. As a 
 consequence\, accessing data from the processing units becomes a bottlenec
 k. To make matters worse\, naive execution models introduce extra pressure
  to memory.\n\nIn this paper\, we discuss three works concerning memory bo
 ttlenecks in analytical processing. The first two examine GPU query proces
 sing. The former eliminates memory-related overheads by removing operator 
 boundaries\, whereas the latter dissects the execution time of queries\, d
 ata transfers included\, on GPUs. The third paper discusses bypassing the 
 multi-core CPU memory bottleneck with higher cache utilization. Then\, we 
 conclude with our vision for high throughput concurrent workload processin
 g on GPUs.\n\nBackground papers\nThe Yin and Yang of Processing Data Wareh
 ousing Queries on GPU Devices\, by Yuan Y.\, et al. Proc. VLDB Endow. 6\, 
 10 (Aug. 2013)\, 817–828.\nMain-memory Scan Sharing for Multi-core CPUs.
  Proc. VLDB Endow\, by Qiao L.\, et al.  1\, 1 (Aug. 2008)\, 610–621.\n
 Kernel Weaver: Automatically Fusing Database Primitives for Efficient GPU 
 Computation\, by  Wu H.\, et al.  In 2012 45th Annual IEEE/ACM Internati
 onal Symposium
LOCATION:BC 229 https://plan.epfl.ch/?room==BC%20229
STATUS:CONFIRMED
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