Fpga Accelerated Analytics
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Fpga-Accelerated Analytics
Datacenters hosting the data-intensive applications used in machine learning and online services are facing an important challenge: the amount of data that needs to be stored and processed is increasing at an exponential rate whereas traditional processor performance has been stagnating for years as Moore's Law tapers off. Driven by these trends, data processing and management applications have become increasingly distributed leading to new data movement bottlenecks at various levels of the software and hardware architecture. The authors show how specialized hardware accelerators can provide an answer to the compute stagnation problem and be helpful in reducing data movement bottlenecks by placing them in the right location within the computer architecture. They concentrate on Field Programmable Gate Arrays (FPGAs) and show how they make it possible to express algorithms in ways that are fundamentally different from CPUs or GPUs. Many major companies are using these accelerator techniques in their storage and processing offerings. The authors discuss the benefits of using FPGAs in the context of analytical processing, both as an accelerator within a single node database and as part of distributed data analytics pipelines. They present guidelines for accelerator design in both scenarios and examples of integration within full-fledged Relational Databases. They do so through the prism of recent research projects that explore how emerging compute-intensive operations in databases can benefit from FPGAs. Finally, they highlight future research challenges in programmability and integration and cover architectural trends that are propelling the rapid adoption of accelerators in datacenters and the cloud. The monograph provides researchers and practitioners a concise insight into how FPGAs can play an important role in designing modern data-intensive computing systems. Drawing on both theory and practical implementations the readers are brought quickly up to speed on a technique that will significantly improve a system's performance.
Security of FPGA-Accelerated Cloud Computing Environments
This book addresses security of FPGA-accelerated cloud computing environments. It presents a comprehensive review of the state-of-the-art in security threats as well as defenses. The book further presents design principles to help in the evaluation and designs of cloud-based FPGA deployments which are secure from information leaks and potential attacks.
Analyzing Analytics
This book aims to achieve the following goals: (1) to provide a high-level survey of key analytics models and algorithms without going into mathematical details; (2) to analyze the usage patterns of these models; and (3) to discuss opportunities for accelerating analytics workloads using software, hardware, and system approaches. The book first describes 14 key analytics models (exemplars) that span data mining, machine learning, and data management domains. For each analytics exemplar, we summarize its computational and runtime patterns and apply the information to evaluate parallelization and acceleration alternatives for that exemplar. Using case studies from important application domains such as deep learning, text analytics, and business intelligence (BI), we demonstrate how various software and hardware acceleration strategies are implemented in practice. This book is intended for both experienced professionals and students who are interested in understanding core algorithms behind analytics workloads. It is designed to serve as a guide for addressing various open problems in accelerating analytics workloads, e.g., new architectural features for supporting analytics workloads, impact on programming models and runtime systems, and designing analytics systems.