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Neuroscribe = نيوروسكرايب
Neuroscribe is a cutting-edge deep learning framework designed to address the complexities and inefficiencies encountered in existing frameworks like PyTorch and TensorFlow. Aimed at streamlining model development and enhancing performance across diverse hardware environments, NeuroScribe offers a lightweight and flexible solution. The framework features a robust tensor library, an auto-differentiation engine, a comprehensive neural network module, and advanced optimization algorithms. With built-in visualization tools and a user-friendly interface, NeuroScribe simplifies both beginner and advanced workflows. Its cross-platform compatibility, supported by CUDA and Metal Performance Shaders (MPS), ensures optimal performance, and in some scenarios, NeuroScribe demonstrates superior speed compared to leading frameworks. Additionally, NeuroScribe introduces unique libraries and features not found in other frameworks, further enhancing its versatility and appeal. The modular architecture and automatic system detection further enhance its adaptability, making NeuroScribe a versatile and powerful tool for deep learning practitioners.
Accelerator Programming Using Directives ; 6th International Workshop, WACCPD 2019, Denver, CO, USA, November 18, 2019, Revised Selected Papers
This book constitutes the refereed post-conference proceedings of the 6th International Workshop on Accelerator Programming Using Directives, WACCPD 2019, held in Denver, CO, USA, in November 2019. The 7 full papers presented have been carefully reviewed and selected from 13 submissions. The papers share knowledge and experiences to program emerging complex parallel computing systems. They are organized in the following three sections: porting scientific applications to heterogeneous architectures using directives; directive-based programming for math libraries; and performance portability for heterogeneous architectures.
