Rocm vs opencl benchmark code#

Nonetheless, if I run mcxcl with half-precision on rocm 1.7.1b3, the speed is still not improving much. The goal of trying to use rocm for mcxcl is to see if we can accelerate our simulation using the half-precision hardware in Vega 64, from what we learned from the AMD OpenCL forum, the only way to use the half-precision units is to use rocm because amdgpu-pro does not have rapid-packed math support. o 3: -cl-mad-enable -DMCX_USE_NATIVE -DMCX_SIMPLIFY_BRANCH -DMCX_VECTOR_INDEX -DMCX_SRC_PENCIL o 1: -cl-mad-enable -DMCX_USE_NATIVE -DMCX_SRC_PENCIL (loss due to initial specular reflection is excluded in the total) /src/mcx_core.cl -n 1000īuilding kernel with option: -cl-mad-enable -DMCX_USE_NATIVE -DMCX_SIMPLIFY_BRANCH -DMCX_VECTOR_INDEX -DMCX_SRC_PENCIL -D MCX_DO_REFLECTION /bin/mcxcl -A -f benchmark1.json -b 1 -P '' -s benchmark2 -k. (loss due to initial specular reflection is excluded in the /usr/bin/oclgrind. Simulated 1000 photons (1000) with 1 devices (repeat x1)

threadph=15 oddphotons=40 np=1000.0 nthread=64 nblock=64 repetition=1 init complete : 0 msīuilding kernel with option: -cl-mad-enable -DMCX_USE_NATIVE -DMCX_SIMPLIFY_BRANCH -DMCX_VECTOR_INDEX -DMCX_SRC_PENCIL

Rocm vs opencl benchmark code#

code name: compiled with OpenCL version = The MCX Project is funded by the NIH/NIGMS under grant R01-GM114365 = = Department of Bioengineering, Northeastern University = = Computational Optics&Translational Imaging (COTI) Lab - = = Copyright (c) 2010-2018 Qianqian Fang = Segment: GLOBAL FLAGS: KERNARG, FINE GRAINED With CUDA, developers can dramatically speed up computing applications by harnessing the power of GPUs.Sig. CUDAĬUDA is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). AMD has also provided a ROCm platform environment for all types of GPUs but the environment is new and does not support all hardware. Installing CUDA for machine learning tasks is like a cakewalk. NVidia has CUDA support for accelerating graphic processing and deep neural network model training tasks. In case you purchased an AMD graphic card then you are going to experience a hard time. If you got NVidia on your laptop then everything will go smoothly for you. But still, AMD managed to produce good GPUs that kind of does the job for you. Both are always been rivals in the field of graphic processors but we all know that NVidia is the undisputed king of this field.ĪMD also has a long history in manufacturing good CPUs but when it comes to GPU AMD doesn’t stand anywhere close to NVidia. When it comes to GPU there two big names NVidia and AMD. If you are the kind of person that loves AI and machine learning and building your own AI model is your next goal then the first and foremost thing is that you must have a laptop backed by a good GPU for training your machine learning model(which I was unaware). GPU considerably reduces the time required for model training. GPUs have a different architecture from CPUs, it works in a parallel way whereas CPU goes sequentially. This is where CPUs sucks and GPUs make its entry.

AI requires lots of computation power for training the model.

We all know Artificial intelligence is the upcoming reality which has raised the demand for processing more and more data to produce meaningful outcomes. I recently started learning AI programming and headed to build my first AI project unaware of the situation that AMD is going to ditch me. I mostly use my laptop for programming purposes. Ok, that’s not a big issue for me as I don’t play a lot of games. Within a few months, I realized that the frame rates drop during gaming and it heats pretty easily. Last year I got my laptop and in a rush, I bought an AMD GPU with it.