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CUDA, Supercomputing for the Masses: Part 6

Global memory and the CUDA profiler - Parallel

CUDA, Supercomputing for the Masses: Part 3

Error handling and global memory performance limitations - Parallel

CUDA, Supercomputing for the Masses: Part 1

CUDA lets you work with familiar programming concepts while developing software that can run on a GPU - Parallel

Atomic Operations and Low-Wait Algorithms in CUDA

Used correctly, atomic operations can help implement a wide range of generic data structures and algorithms in the massively threaded GPU programming environment. However, incorrect usage can turn massively parallel GPUs into poorly performing sequential processors. - Parallel

CUDA vs. Phi: Phi Programming for CUDA Developers

Both CUDA and Phi coprocessors provide high degrees of parallelism that can deliver excellent application performance. For the most part, CUDA programmers with existing application code have already written their software so it can run well on Phi coprocessors. However, additional work may be required to achieve the highest possible performance. - Parallel

Easy GPU Parallelism with OpenACC

An emerging standard uses pragmas to move parallel computations in C/C++ and Fortran to the GPU - Parallel

Intel's 50+ core MIC architecture: HPC on a Card or Massive Co-Processor?

Will Intel’s Knights Corner chips function as co-processors like GPUs, or will they be stand-alone many-core Linux systems? The two approaches present very different performance profiles. - Parallel

CUDA, Supercomputing for the Masses: Part 19

Parallel Nsight Part 1: Configuring and Debugging Applications - Parallel

CUDA, Supercomputing for the Masses: Part 14

Debugging CUDA and using CUDA-GDB - Parallel

CUDA, Supercomputing for the Masses: Part 13

Using texture memory in CUDA - Parallel

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