#ROCM VS OPENCL BENCHMARK SOFTWARE#
#ROCM VS OPENCL BENCHMARK SERIES#

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.

#ROCM VS OPENCL BENCHMARK SOFTWARE#

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. It does not store any personal data.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).

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Figure 6: Seismic Benchmarks for CUDA Codes.

Figure 4: FirePro S9300 x2 Board with Fiji GPU.

#ROCM VS OPENCL BENCHMARK SERIES#

Figure 3: AMD Radeon S Series Compute GPUs.Figure 2: AMD Single Source Development Environment.Figure 1: AMD ROCm Improves Latency to Compute.AMD Hardware for Compute-Intensive Workloads.ml Open for Machine Learning on AMD Hardware.Anaconda Python Acceleration with Numba from Continuum.HIP: Heterogeneous-Compute Interface for Portability.AMD HCC: Single Source Heterogeneous Compute Compiler.ROCr: Heterogeneous System Architecture Runtime Support on Discrete GPUs.

ROCm: Open HPC for Heterogeneous Computation.

This paper examines AMD’s HPC software strategy and the capabilities of AMD’s product portfolio, and it makes recommendations for users considering gear for HPC and deep learning applications. ROCm is an open source suite of drivers, tools, and libraries designed for a variety of programming models, including programs written to the NVIDIA CUDA proprietary programming interface. Advanced Micro Devices (AMD) recently launched a software initiative called Radeon Open Compute Platform (ROCm) to help support GPUs in high performance computing (HPC) and deep learning applications.

Rocm vs opencl benchmark