Tags » GPU Computing

Dusting off cometary surfaces: collimated jets despite a homogeneous emission pattern.

Knowledge of GPGPU techniques is helpful for rapid model building and testing of scientific ideas. For example, the beautiful pictures taken by the ESA/Rosetta spacecraft of comet 67P/Churyumov–Gerasimenko reveal jets of dust particles emitted from the comet. 513 more words

GPU

NVIDIA GPU Computing is Out of Control!

I’m interrupting the Yogic View of Consciousness series to recommend that people interested in computer technology check out Jen-Hsun Huang’s Keynote talk from this year’s 2015 NVIDIA 2015 GPU Technology Conference.  1,137 more words

Yoga

Preparing for classes


This year, again, my teaching is in the second semester. So now the holidays are over, I’m busy making final preparations for the coming semester. I’ll be teaching a class on computer architecture, specifically focusing on current parallel systems. 38 more words

Computers

Slow or fast transfer: bottleneck states in light-harvesting complexes

In the previous post I described some of the computational challenges for modeling energy transfer in the light harvesting complex II (LHCII) found in spinach. Here, I discuss the… 391 more words

GPU

High-performance OpenCL code for modeling energy transfer in spinach

With increasing computational power of massively-parallel computers, a more accurate modeling of the energy-transfer dynamics in larger and more complex photosynthetic systems (=light-harvesting complexes) becomes feasible – provided we choose the right algorithms and tools. 555 more words

GPU

Commentary: GPU vs. CPU comparison done right

I have in earlier posts complained about how some researchers, through unfair comparisons, make GPU computing look more attractive than it really is.

It is thus only appropriate to also commend those who do it right. 371 more words

Commentary

Parallel Programming ( Vector Addition On OpenCL )

Introduction

OpenCL (Open Computing Language) is a new framework for writing programs that execute in parallel on different compute devices (such as CPUs and GPUs) from different vendors (AMD, Intel, ATI, Nvidia etc.). 695 more words