Tag Archives: gpu

NVIDIA Tesla K20 GPU Accelerator (Kepler GK110) Up Close

Posted on August 22, 2013 by Eliot Eshelman

NVIDIA’s Tesla K20 GPU is currently the de facto standard for high-performance heterogeneous computing. Based upon the Kepler GK110 architecture, these are the GPUs you want if you’ll be taking advantage of the latest advancements available in CUDA 5.0 and … Continue reading →

GPU Memory Types – Performance Comparison

Posted on August 6, 2013 by Justin McKennon (for Microway)

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 device has its … Continue reading →

Optimize CUDA Host/Device Transfers

Posted on July 3, 2013 by Justin McKennon (for Microway)

This post is Topic #2 (part 2) in our series Parallel Code: Maximizing your Performance Potential. In my previous post, CUDA Host/Device Transfers and Data Movement, I provided an introduction into the bottlenecks associated with host/device transfers and data movement. … Continue reading →

CUDA Host/Device Transfers and Data Movement

Posted on June 24, 2013 by Justin McKennon (for Microway)

This post is Topic #2 (part 1) in our series Parallel Code: Maximizing your Performance Potential. In post #1, I discussed a few ways to optimize the performance of your application via controlling your threads and provided some insight as … Continue reading →

CUDA Parallel Thread Management

Posted on June 13, 2013 by Justin McKennon (for Microway)

This post is Topic #1 in our series Parallel Code: Maximizing your Performance Potential. Regardless of the environment or architecture you are using, one thing is certain: you must properly manage the threads running in your application to optimize performance. This … Continue reading →

Parallel Code: Maximizing your Performance Potential

Posted on June 3, 2013 by Justin McKennon (for Microway)

No matter what the purpose of your application is, one thing is certain. You want to get the most bang for your buck. You see research papers being published and presented making claims of tremendous speed increases by running algorithms … Continue reading →

5 Easy First Steps on GPUs – Accelerating Your Applications

Posted on April 26, 2013 by Eliot Eshelman

This week NVIDIA provided a tutorial outlining first steps for GPU acceleration using OpenACC and CUDA. This was offered as part of the “GPUs Accelerating Research” week at Northeastern University and Boston University. After attending, it seemed appropriate to review … Continue reading →

NVIDIA Tesla K10 GPU Accelerator (Kepler GK104) Up Close

Posted on September 7, 2012 by Eliot Eshelman

NVIDIA is now shipping their 4.58 TFLOPS single-precision floating point GPUs. The Tesla K10 GPU Accelerators, based upon the Kepler GK104 architecture, are the first Teslas available from this new generation of products. They are designed for single-precision float-point applications, … Continue reading →

Inside NVIDIA Kepler – Live from GTC 2012

Posted on May 16, 2012 by Eliot Eshelman

Compute performance has been exponentially increasing for the entirety of your life – it doesn’t matter what your age is. This week at NVIDIA’s GTC 2012 conference, we’ve seen that GPUs are still leading the charge. The new NVIDIA “Kepler” … Continue reading →

nvidia-smi: Control Your GPUs

Posted on December 5, 2011 by Eliot Eshelman

This post was last updated on 2018-11-05 Most users know how to check the status of their CPUs, see how much system memory is free, or find out how much disk space is free. In contrast, keeping tabs on the … Continue reading →