Titlebook: Introduction to HPC with MPI for Data Science; Frank Nielsen Textbook 2016 Springer International Publishing Switzerland 2016 Data Science

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令人作呕

Textbook 2016 a first course for undergraduates on parallel programming on distributed memory models, and requires only basic programming notions..Divided into two parts the first part covers high performance computing using C++ with the Message Passing Interface (MPI) standard followed by a second part providin

痛恨

Topology of Interconnection Networksen nodes on the physical network for neighbor nodes on the logical network) and the expansion (defined as the ratio of the number of nodes in the physical network over the number of nodes in the logical network).

contrast-medium

Hierarchical Clusteringotonous if and only if the similarity decreases along the path from any leaf to the root, otherwise there exists at least one inversion. The single, complete, and average linkage criteria guarantee the monotonic property, but not the often used Ward’s criterion.

吞下

Introduction to MPI: The Message Passing Interface the prominent OpenMPI, MPICH2, etc.). Communications can either be synchronous or asynchronous, bufferized or not bufferized, and one can define synchronization barriers where all processes have to wait for each other before further carrying computations.

门闩

The MapReduce Paradigmrocessing massive data sets on a cluster of computers, and a platform to execute and monitor jobs. MapReduce is straightforward to use, can be easily extended, and even more importantly MapReduce is prone to both hardware and software failures.

mechanical

Fast Approximate Optimization in High Dimensions with Core-Sets and Fast Dimension Reductionnder some stability hypothesis to point perturbations of the optimal clustering, an approximation of the minimization of the cost function (instead of the regular .-means objective function) will end up with a good (if not an optimal) clustering.

Plaque

Supervised Learning: Practice and Theory of Classification with the ,-NN Rule, the .-NN classification rule can be easily parallelized on a distributed memory architecture like a computer cluster. One of the drawback of the .-NN rule is that it needs to store all the training set in order to classify new observations.

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