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Category Archives: Development
GPU Shared Memory Performance Optimization
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. Now that you’re … Continue reading
GPU Memory Types – Performance Comparison
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
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
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
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
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
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
Software Support for Intel® Xeon Phi™ Coprocessors
With Intel’s release of the Xeon Phi coprocessor cards, HPC users must ask themselves how much performance they need and how they plan to achieve it. Will resources be devoted towards fast new hardware, re-writing/optimizing software or some balance of … Continue reading
GPU Performance without GPU Coding
I think everyone in the HPC arena has heard plenty about GPUs. GPUs aren’t sophisticated like CPUs, but they provide raw performance for those who know how to use them. The question for those who have large computational workloads has … Continue reading
