NVIDIA has announced a new version of their popular Tesla M40 GPU – one with 24GB of high-speed GDDR5 memory. The name hasn’t really changed – the new GPU is named NVIDIA Tesla M40 24GB. If you…
Comprised of a set of compiler directives, OpenACC was created to accelerate code using the many streaming multiprocessors (SM) present on a GPU. Similar to how OpenMP is used for accelerating code on multicore CPUs, OpenACC can…
With the release of Tesla M40, NVIDIA continues to diversify its professional compute GPU lineup. Designed specifically for Deep Learning applications, the M40 provides 7 TFLOPS of single-precision floating point performance and 12GB of high-speed GDDR5 memory.…
In my previous post, I presented a brief introduction to the CUB library of CUDA primitives written by Duane Merrill of NVIDIA. CUB provides a set of highly-configurable software components, which include warp- and block-level kernel components…
CUB – a configurable C++ template library of high-performance CUDA primitives Each new generation of NVIDIA GPUs brings with it a dramatic increase in compute power and the pace of development over the past several years has…
NVIDIA’s latest Tesla accelerator is without a doubt the most powerful GPU available. With almost 3,000 CUDA cores and 12GB GDDR5 memory, it wins in practically every* performance test you’ll see. As with the “Kepler” K20 GPUs,…
The debut of NVIDIA’s Kepler architecture in 2012 marked a significant milestone in the evolution of general-purpose GPU computing. In particular, Kepler GK110 (compute capability 3.5) brought unrivaled compute power and introduced a number of new features…
This post is Topic #3 (post 3) in our series Parallel Code: Maximizing your Performance Potential. Many applications contain algorithms which make use of multi-dimensional arrays (or matrices). For cases where threads need to index the higher…
This post is Topic #3 (post 2) in our series Parallel Code: Maximizing your Performance Potential. In my previous post, I provided an introduction to the various types of memory available for use in a CUDA application.…
This post is Topic #3 (part 1) in our series Parallel Code: Maximizing your Performance Potential. CUDA devices have several different memory spaces: Global, local, texture, constant, shared and register memory. Each type of memory on the…
