]]>
]]>In the previous two posts we looked at how to move data efficiently between the host and device. In this sixth post of our CUDA C/C++ series we discuss how to efficiently access device memory, in particular global memory, from within kernels. There are several kinds of memory on a CUDA device, each with different scope, lifetime, and caching behavior. So far in this series we have used global��
]]>
In the previous two posts we looked at how to move data efficiently between the host and device. In this sixth post of our CUDA Fortran series we discuss how to efficiently access device memory, in particular global memory, from within kernels. There are several kinds of memory on a CUDA device, each with different scope, lifetime, and caching behavior. So far in this series we have used global��
]]>In the previous three posts of this CUDA C & C++ series we laid the groundwork for the major thrust of the series: how to optimize CUDA C/C++ code. In this and the following post we begin our discussion of code optimization with how to efficiently transfer data between the host and device. The peak bandwidth between the device memory and the GPU is much higher (144 GB/s on the NVIDIA Tesla C2050��
]]>In the previous three posts of this CUDA Fortran series we laid the groundwork for the major thrust of the series: how to optimize CUDA Fortran code. In this and the following post we begin our discussion of code optimization with how to efficiently transfer data between the host and device. The peak bandwidth between the device memory and the GPU is much higher (144 GB/s on the NVIDIA Tesla C2050��
]]>In the first post of this series we looked at the basic elements of CUDA C/C++ by examining a CUDA C/C++ implementation of SAXPY. In this second post we discuss how to analyze the performance of this and other CUDA C/C++ codes. We will rely on these performance measurement techniques in future posts where performance optimization will be increasingly important. CUDA performance measurement is��
]]>In the first post of this series we looked at the basic elements of CUDA Fortran by examining a CUDA Fortran implementation of SAXPY. In this second post we discuss how to analyze the performance of this and other CUDA Fortran codes. We will rely on these performance measurement techniques in future posts where performance optimization will be increasingly important.
]]>This post is the first in a series on CUDA Fortran, which is the Fortran interface to the CUDA parallel computing platform. If you are familiar with CUDA C, then you are already well on your way to using CUDA Fortran as it is based on the CUDA C runtime API. There are a few differences in how CUDA concepts are expressed using Fortran 90 constructs, but the programming model for both CUDA Fortran��
]]>
After a recent talk I gave called ��CUDA 101: Intro to GPU Computing��, a student asked ��What��s the best way for me to get experience in parallel programming and CUDA?��. This is a question I struggled a lot with when I was in college and one I still ask myself about various topics today. The first step is to realize that it��s hard to get useful experience without having some skill in an area.
]]>