Authors: Kenneth S.Bogh, Sean Chester, Ira Assent (Data-Intensive Systems Group, Aarhus University). Type: Research Paper Presented by: Dardan Xhymshiti Fall 2015

 Introduction  Skyline computation  Related-Work  GPU-Friendly partitioning  The SKYALIGN algorithm  Experimental evaluation

 Skyline operator: First introduced: Stephan Borzsonyi, Donald Kossman, Konrad Stocker 2001 (Universitat Passau & Technische Universitat Muncen Germany)

 Skyline operator:  Example:  1. Go for a one day skiing in one of the Colorado’s ski center.  2. You have spent a lot of money.  3. It happens a car defect.  4. Try to find the nearest and cheapest hotel.  5. Take your phone and lunch the unknown touristic application.  6. A lot of hotels in different locations with variety of prices.  7. You want to find the CHEAPEST and the NEAREST one!?

 Skyline operator:  Example:  Query results: Result queryPriceDistance (Miles) Hotel A$ Hotel B$1401 Hotel C$2002 Hotel D$ ….……

 Skyline operator:  Example:  Query results: Result queryPriceDistance (Miles) Hotel A$ Hotel B$1401 Hotel C$2002 Hotel D$ ….…… Skyline set = {Hotel A, Hotel B, Hotel D} Term: Dominance

 Major problems:  Multidimensional data.  Computation intensive.  Comparison tuple-to-tuple (point-to-point).  What is done till now:  State-of-the art sequential algorithms.  Parallel skyline query processing algorithms.  Often try to achieve device’s maximum theoretical compute throughput.  Throughput is costly. The most efficient GPU algorithm GSS, does up to 650 times more work comparing to the best sequential algorithm, even if executing in 2688 cores.  For benchmark datasets, sequential algorithms perform 3x faster than GPU ones.  Should we use GPU or NOT?

 Sequential algorithms high performance is achieved by using:  Trees  Recursion  Strict ordering of computation.  Unpredictable branching.  Motivation:  Come up with a new algorithm called SkyAlign which:  MAIN POINT: Avoid as much as it can point-to-point comparisons.  Employ a globally static grid schema to make the dataset compatible for GPU.  This algorithm do not maximizes THROUGHPUT but is WORK-EFFICIENT. Many of these techniques are not compatible with GPU.

Dataset Skyline set Parallel DatasetSkyline set Sequential VS

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 GPU Computation  Tesla K80: 4992 number of Cuda Cores.  Threads are grouped into warps usually of sizes 32.  Warps are grouped into thread blocks.  All threads within a warp execute the same instruction at the same time.  Problem: branch divergence.

 Partition-based skyline algorithms  Divide-and-Conquer: Halved the dataspace recursively by the median of an arbitrarily chosen dimension and solved each half. After that the results are merged.  Sequential partition-based algorithms: These algorithms employ recursive, point-based partitioning. For each partition defined, a skyline point (pivot), is found, and the other points are partitioned based on their relationship to the pivot. The work performed varies from the pivot selected. SkyAlign: is a partition-based method, but it is not recursive and has no merge.

Get to know with point-based methods  Point-based recursive partitioning methods use a quad-tree partitioning of the data set and record skyline points as they are found in a tree. C B E A D F

Why recursive partitioning is not preferred?  High divergence Traversal Consider when points in F are to compare with points in D. First a DT with the root E is performed for each point, so generating bitmasks. These bitmasks are then used to determine which branches of D each point of F should traverse. Results often diverge. Partitioning Each partition has to be sub-partitioned relative to its own pivot. The pivot needs to be skyline. High dimensions Quad-tree partitioning do not scale well with dimensionality.

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Mask assignment: Masks are assigned for each point, given the quartiles of the dataset for each dimension.

Data sorting: Sort the data points based on their masks order.

Data sorting: Sort the data points based on their masks order.

Work-efficiency Compare the performance of the four algorithms with respect to: 1. Dominance tests (DT) 2. Work-efficiency

Work-efficiency Compare the performance of the four algorithms with respect to: 1. Dominance tests (DT) 2. Work-efficiency

Thank You