#Nvprof cudalaunch code#
This is a limitation because the current version of GPGPU-Sim only supports executing SASS code for older generation GPUs. Instead, the cuDNN library appears to contain hand tuned machine-level SASS assembly code for supporting tensor cores. One limitation of this work is a lack of support for NVIDIA’s tensor cores which is a consequence of the fact that the intermediate-level PTX assembly code embedded within NVIDIA’s cuDNN library does not include tensor core operations. In this paper, we focus on enabling support for cuDNN as cuDNN enables the highest performance on NVIDIA GPUs via implementation of specialized algorithms such as Winograd. Indeed, we confirmed with the maintainers of GPGPU-Sim that a key limitation of the currently available version of GPGPU-Sim is the lack of support for applications that use precompiled libraries. As a result, popular open source GPU architecture simulators such as GPGPU-Sim are unable to run applications that make use of these precompiled libraries. These libraries take advantage of the vendor’s detailed knowledge of their product’s microarchitecture, which is typically not fully described in publicly available documentation. To achieve the highest levels of performance these libraries are typically provided by hardware vendors. Calls to this API invoke computation on a GPU via specialized precompiled libraries such as cuBLAS and cuDNN.
Popular machine learning frameworks such as TensorFlow and PyTorch typically expose a high-level python application programming interface (API) to developers.
This paper takes an important step towards addressing this shortcoming. The strong potential for neural network deployment in mobile platforms (e.g., iPhone X, Huawei) and small embedded devices, another reason for the lack of academic research on optimizing GPUs for machine learning may be the lack of support in current architecture simulators for running these workloads. Although the focus of academic researchers is to exploit While industry has rapidly introduced changes to GPU architectures to support machine learning training, such as Tensor Cores and NVLINK introduced in the NVIDIA Volta architecture, academic researchers have largely focused on designing inference accelerators. Training DNNs requires massive amounts of computational power, which is currently predominantly done with graphics processor units (GPUs). In recent years deep neural networks (DNNs) have made striking advances in accuracy. Machine learning is being employed to tackle a rapidly growing set of problems.
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