Abstract This chapter starts with a high-level characterization of data center energy before moving to a discussion of power, energy, and sustainability in warehouse-scale computers (WSCs). We discuss the economic and environmental importance of energy efficiency, and define metrics like PUE (Power Usage Effectiveness) and SPUE (Server PUE) that help assess and improve data center and server infrastructure efficiency. We also explore the concept of energy-proportional computing and techniques for enhancing the energy efficiency of computing resources. We then discuss data center power provisioning strategies, including oversubscription and energy storage, and conclude with a discussion of environmental sustainability and a high-level characterization of data center energy usage of the internet and cryptocurrency mining.

Abstract This chapter introduces warehouse-scale computers (WSCs) and discusses how they underpin the ongoing shift to cloud computing. WSCs represent a separate class of computing systems distinct from traditional data centers, and present unique challenges around scale, cost efficiency, reliability, and energy efficiency. We conclude with an outline for the rest of the book.

A framework has multiple types of compilers: the computation graph optimizer, the primitive libraries JIT to select the best schedule, the code generation path for operations not supported by the primitive libraries, and the other DL compilers covered in Sections 9.4–9.9. A computation graph is a high-level graph that represents the computations, data flow, and control-flow of a DL program (a model). Each node typically corresponds to a tensor operator (op), such as conv2d, GEMM, or sigmoid. Some nodes represent control-flow operators to enable conditional branches and loops on different parts of the computation graph. The edges represent the data flow and tensor dependencies between operators, as illustrated in Figure 1.5. A tensor is a multidimensional array; a scalar, vector, and matrix are 0D, 1D, and 2D tensors, respectively. 3D, 4D, and 5D tensors are also common.

Motion planning is the module that computes how a robot or autonomous vehicle maneuvers itself. The task of motion planning is to generate a trajectory without colliding any obstacles and sends it to the feedback control for physical robot control execution. The planned trajectory is usually specified and represented as a sequence of planned trajectory points. Each of these points contains attributes like location, time, speed, etc. Usually, the motion planning module is followed by the control module, which is used to track the differences between the actual pose and the pose on the pre-defined trajectory by continuous feedback, which has no essential difference with general mechanic control.

This book targets computer scientists and engineers who are familiar with concepts in classical computer systems but are curious to learn the general architecture of quantum computing systems. It gives a concise presentation of this new paradigm of computing from a computer systems' point of view without assuming any background in quantum mechanics. As such, it is divided into two parts. The first part of the book provides a gentle overview on the fundamental principles of the quantum theory and their implications for computing. The second part is devoted to state-of-the-art research in designing practical quantum programs, building a scalable software systems stack, and controlling quantum hardware components. Most chapters end with a summary and an outlook for future directions. This book celebrates the remarkable progress that scientists across disciplines have made in the past decades and reveals what roles computer scientists and engineers can play to enable practical-scale quantum computing.

Abstract Many modern computer systems, including homogeneous and heterogeneous architectures, support shared memory in hardware. In a shared memory system, each of the processor cores may read and ...