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- #Rocm vs opencl benchmark manual#
- #Rocm vs opencl benchmark software#
- #Rocm vs opencl benchmark series#
A fully manual port of the framework to an OpenCL-equivalent version, initially undertaken by the company, required modification of more than half of that source code and took approximately a developer half-year. According to Stoner, the currently published version of CUDA-accelerated Caffe contains more than 55,000 lines of source code. Of course, AMD will bear the burden of keeping its toolset up to date as CUDA and its associated APIs continue to evolve.Īs an example of HIPify's effectiveness, Stoner described a recent internal AMD project to port the GPU-accelerated, CUDA-based variant of the popular Caffe deep learning framework ( Figure 3). When pressed about the legal ramifications of such a tool, Stoner pointed to the recent Oracle-vs-Google Java trial, which he claimed clarified the fair use of published APIs.
![rocm vs opencl benchmark rocm vs opencl benchmark](https://www.phoronix.com/data/img/results/sapphire_hd4550/10.png)
And the HIPify toolset enables developers to convert existing CUDA source code to a HIP-compatible equivalent, which according to Stoner also remains CUDA-compatible.
#Rocm vs opencl benchmark series#
A series of published HIP APIs allow developers to program for AMD GPUs in a CUDA-like schema. NVIDIA's (and CUDA's) present supremacy directly leads to another aspect of AMD's toolset: HIP (the Heterogeneous-compute Interface for Portability).
#Rocm vs opencl benchmark software#
ROCm's various software tools and partnerships span the support spectrum from assembly language through Python, and are intended to address various developers' needs.
![rocm vs opencl benchmark rocm vs opencl benchmark](https://www.phoronix.com/data/img/results/nvidia_fx1700_oc/02.png)
This mix may seem curious, given that OpenCL v1.2 dates back to 2011, but it's a pragmatic "nod" to competitor NVIDIA's current dominance in the HPC space, along with NVIDIA's reluctance to embrace anything beyond OpenCL v1.2.įigure 2. First is its OpenCL support, which Stoner describes as "OpenCL 1.2+" in its current form based on an OpenCL v1.2 compatible runtime, along with a select subset of the OpenCL 2.0 kernel language enhancements. Perhaps obviously, servers require support for standalone graphics chips and boards, which is why (as InsideDSP's recent HSA coverage noted) ROCm expands beyond HSA standards in a proprietary fashion.Īccording to Gregory Stoner, AMD's Senior Director for Radeon Open Compute, ROCm (previously known by its "Boltzmann Initiative" project name), comprises a collection of technologies for efficiently harnessing GPU coprocessors ( Figure 2). Also, to date the HSA Foundation's specifications focus on graphics cores implemented alongside processor cores on unified SoCs such as in AMD's APUs (accelerated processing units). In contrast, CUDA has supported programmer-friendly C++ since its mid-2007 initial release. The OpenCL C++ kernel language will be fully integrated into the core specification with OpenCL v2.2 (now under development). For example, only with the late-2015 release of OpenCL v2.1 did the standard replace the legacy OpenCL C kernel language with OpenCL C++ (a subset of C++14), and then only in a provisional fashion. But relying on standards has sometimes been a hindrance. Historically, in contrast to NVIDIA's proprietary CUDA approach, AMD has elected to rely on industry-standard heterogeneous processing approaches such as OpenCL, along with the HSA Foundation's various efforts. First presented at the 2015 Supercomputing Conference, AMD's ROCm has come a long way in a year's time. AMD acknowledges that it is behind, and the company's ROCm (Radeon Open Compute Platform) initiative, conceptually unveiled at last year's Supercomputing Conference and disclosed in a more fully implemented form at last month's SC16, is key to its planned catch-up ( Figure 1).įigure 1. However, GPGPU has been embraced in the HPC (high-performance computing) server space, and NVIDIA is the dominant supplier of GPUs for HPC. The company's CUDA software toolset for GPU computing has to date secured only modest success in mobile and desktop PCs with game physics processing acceleration, for example, along with still and video image processing acceleration. NVIDIA was an early and aggressive advocate of leveraging graphics processors for other massively parallel processing tasks (often referred to as general-purpose computing on graphics processing units, or GPGPU).