Massive parallel programming on GPU devices for Big Data.

• Introduction to the linux systems in the computer room

• Cuda SDK components (libraries, nvcc, nsight)

• Cuda architecture (SIMT principle + memory design)

• Basic cuda language extensions

• Basic programming examples (vector addition in parallel etc)

• Principle of random number generation in parallel (Skip ahead vs batch approach)

• Linear congruential random number generators

• CURAND library and XORShift generators

• Vector summation in parallel (reduction principle)

• Concurrency and atomic operations

• Monte-Carlo simulation for pricing derivates

• Stochastic gradient algorithm for GPUs (HOGWILD)

• Parallel design for word2vec algorithm with negative sampling

• Introduction to cuDNN

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