"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.
