Data-Parallel Operations: Scala Programming

Related lectures (30)

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Covers data-parallel programming basics and rendering the Mandelbrot set efficiently.

Covers the basics of parallel programming, including exploiting parallelism in algorithms and the importance of avoiding race conditions.

Explores data-parallel operations in Scala, converting collections, implementing sum, and examining fold operations.

Covers multiprocessor architecture, sustainable computing, AI model training impact, and parallel programming fundamentals.

Explores data-parallel programming with vector processors and SIMD, and introduces MapReduce, Pregel, and TensorFlow.

Covers the evolution and challenges of multiprocessors, emphasizing energy efficiency, parallel programming, cache coherence, and the role of GPUs.

Covers first-class tasks for flexible parallel computation in Scala.

Explores task parallelism, explicit communication, and synchronization primitives for efficient parallel programming.