Browsing by Author "Ding, W."

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    Cache hierarchy-aware query mapping on emerging multicore architectures
    (IEEE, 2017) Öztürk, Özcan; Orhan, U.; Ding, W.; Yedlapalli, P.; Kandemir, M. T.
    One of the important characteristics of emerging multicores/manycores is the existence of 'shared on-chip caches,' through which different threads/processes can share data (help each other) or displace each other's data (hurt each other). Most of current commercial multicore systems on the market have on-chip cache hierarchies with multiple layers (typically, in the form of L1, L2 and L3, the last two being either fully or partially shared). In the context of database workloads, exploiting full potential of these caches can be critical. Motivated by this observation, our main contribution in this work is to present and experimentally evaluate a cache hierarchy-aware query mapping scheme targeting workloads that consist of batch queries to be executed on emerging multicores. Our proposed scheme distributes a given batch of queries across the cores of a target multicore architecture based on the affinity relations among the queries. The primary goal behind this scheme is to maximize the utilization of the underlying on-chip cache hierarchy while keeping the load nearly balanced across domain affinities. Each domain affinity in this context corresponds to a cache structure bounded by a particular level of the cache hierarchy. A graph partitioning-based method is employed to distribute queries across cores, and an integer linear programming (ILP) formulation is used to address locality and load balancing concerns. We evaluate our scheme using the TPC-H benchmarks on an Intel Xeon based multicore. Our solution achieves up to 25 percent improvement in individual query execution times and 15-19 percent improvement in throughput over the default Linux-based process scheduler. © 1968-2012 IEEE.
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    A cache topology-aware multi-query scheduler for multicore architectures
    (IEEE, 2014) Orhan, U.; Ding, W.; Yedlapalli, P.; Kandemir, M.; Öztürk, Özcan
    Growing performance gap between processors and main memory has made it worthwhile to consider off-chip data accesses in multi-query processing [2], [1], [3]. Exploiting data-sharing opportunities among concurrent queries can be critical for effective utilization of the underlying shared memory hierarchy. Given a set of queries, there may be a common retrieval operation for several cases to the same data. A query can benefit from the data previously loaded into the shared cache/memory space by another query. However, if these queries are scheduled independently, it is very likely that the same data is brought from off-chip memory to on-chip caches multiple times, thereby consuming off-chip bandwidth and slowing down overall execution.
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    Sixth International Workshop on spatial and spatiotemporal data mining: preface
    (2011) Shekar, S.; Agouris, P.; Vatsavai, R. R.; Stefanidis, A.; Bhaduri, B.; Chandola, V.; Aksoy, S.; Appice, A.; Atzori, M.; Baumann, P.; Bogorny, V.; Boriah, S.; Cheriyadat, A.; Croitoru, A.; Cuzzocrea, A.; Deng, K.; Ding, W.; Durbha, S.; Filippi, A.; Franzese, P.; Gunopulos, D.; Ganguly, A.; Gutierrez, A. G.; Guo, D.; Hoffman, F.; Janeja, V.; Jun, G.; Kao, Shih-Chieh; Keogh, E.; Li, Ki-Joune; Lozano, A.; Manco, G.; May, M.; McGovern, A.; Mignet, L.; Mokbel, M.; Pagh, R.; Panait, L.; Papadias, D.; Ratanamahatana, C. A.; Rafaeta, A.; Schmid, F.; Sorokine, A.