Efficient Kernel Optimization with TVM Auto-tuning (eBook, ePUB)
The Complete Guide for Developers and Engineers
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"Efficient Kernel Optimization with TVM Auto-tuning" "Efficient Kernel Optimization with TVM Auto-tuning" is a comprehensive, authoritative guide to boosting computational efficiency at the kernel level using TVM's powerful auto-tuning capabilities. This book establishes a rigorous foundation in both the theoretical and practical aspects of kernel optimization, beginning with the significance of performance for deep learning, high-performance computing, and edge deployments. Readers are introduced to the architecture and modular design of TVM, the challenges of manual kernel tuning, and the ev...
"Efficient Kernel Optimization with TVM Auto-tuning" "Efficient Kernel Optimization with TVM Auto-tuning" is a comprehensive, authoritative guide to boosting computational efficiency at the kernel level using TVM's powerful auto-tuning capabilities. This book establishes a rigorous foundation in both the theoretical and practical aspects of kernel optimization, beginning with the significance of performance for deep learning, high-performance computing, and edge deployments. Readers are introduced to the architecture and modular design of TVM, the challenges of manual kernel tuning, and the evolution of auto-tuning methodologies, making it ideal for advanced practitioners and researchers seeking an in-depth understanding of this rapidly advancing field. Diving deeper, the text navigates through TVM's intermediate representation and scheduling primitives, unpacks the theory behind auto-tuning search spaces and cost modeling, and illuminates the decision-making processes that drive efficient code generation on heterogeneous hardware. With hands-on chapters detailing the configuration and orchestration of TVM's auto-scheduler, the book guides readers through advanced scheduling, memory transformation techniques, and performance modeling with cutting-edge machine learning approaches. Rich case studies demonstrate auto-tuning pipelines for popular deep learning kernels-including matrix multiplications, convolutions, and attention mechanisms-while also addressing optimization for sparse, quantized, or custom operators. Beyond technical mastery, this volume is a practical companion for engineers and researchers scaling their workflows to new domains and hardware. It covers integration with third-party compilers, cross-compilation, distributed and cloud-based tuning, and concludes with best practices, pitfalls, and a look at emerging research frontiers. Both a reference and a roadmap, "Efficient Kernel Optimization with TVM Auto-tuning" is essential reading for those striving for state-of-the-art performance and reliability in modern computational workloads.
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