- freely available
Algorithms 2013, 6(4), 747-761; https://doi.org/10.3390/a6040747
2. Related Work
2.1. Gradual Pattern Mining and Fuzzy Orderings
2.2. Parallel Data Mining
- Distributed memory systems (each processor has its own system memory that cannot be accessed by other processors, the shared data are transferred usually by message passing, e.g., sockets or message passing interface (MPI));
- Shared memory systems where processors share the global memory, they have direct access to the entire set of data. Here, accessing the same data simultaneously from different instruction streams requires synchronization and sequential memory operations;
- Hierarchical systems (a combination of shared and distributed models, composed by multiprocessor nodes in which memory is shared by intra-node processors and distributed over inter-node processors).
3. Parallel Fuzzy Gradual Pattern Mining Based on Multi-Precision Fuzzy Orderings
3.1. Managing Multi-Precision
|Algorithm 1 Fuzzy Orderings-based Gradual Itemset Mining|
3.2. Coupling Multi-Precision and Parallel Programming
4.1. Databases and Computing Resources
- an IBM dx360 M3 server embedding computing nodes configured with 2 × 2.66 GHx six core Intel (WESTMERE) processors, 24 Go DDR3 1,066 Mhz RAM and Infiniband (40 Gb/s) (reported as Intel); and
- an IBM x3850 X5 server running 8 processors embedding ten INTEL cores (WESTMERE), representing 80 cores at 2.26 GHz, 1 To DDR3 memory (1,066 Mhz) and Infiniband (40 Gb/s) reported as SMP (because of its shared memory).
4.2. Measuring Performances
- p is the number of processors/cores or threads;
- T(1) is the execution time of the sequential program (with one thread or core);
- T(p) is the execution time of the parallel program with p processors, cores, or threads.
4.3. Main Results
Conflicts of Interest
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