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Computer Architecture
Computer Architecture This course trains students to use tools like Icarus Verilog, GNU Toolchain, and GTKWave for labs. Students write Armv8-A AArch64 assembly, simulate using Arm Education Core, analyze instruction encoding, implement pipeline stages, resolve RAW hazards, handle control hazards, and estimate Power, Performance, and Area metrics effectively.
Course Chapters
An Introduction to Computer Architecture
Computer architecture involves the design and organization of a computer system's components, focusing on how these elements interact to process instructions efficiently. Through historical milestones, foundational components, design principles, performance metrics, and current trends, the chapter outlines the essential concepts that inform the development of modern computing systems. As technology evolves, architects are urged to consider various factors like energy efficiency and scalability to meet contemporary computing demands.
Fundamentals of Computer Design
Computer design principles address the trade-offs between cost, performance, and energy efficiency. Key factors influencing design decisions include modularity, scalability, and various performance metrics. The chapter explores the system design process, basic design styles, memory hierarchy, and the importance of energy efficiency in modern computing. Future trends highlight innovations such as quantum computing and AI-driven architectures.
Pipelining
Pipelining is a technique that enhances processor performance by overlapping instruction execution across multiple stages, leading to increased throughput. Various types of pipelining, including instruction and data pipelining, help manage the flow of instructions within the CPU. Challenges such as pipeline hazards and stalls are addressed through techniques like forwarding and branch prediction. Advanced pipelining methods continue to evolve, allowing for improvements in efficiency and performance metrics like throughput, latency, and speedup.
Branches and Limits to Pipelining
Branching is a critical aspect of pipelined architectures that impacts performance due to the challenges posed by control hazards. Techniques such as branch prediction, delay slots, and out-of-order execution help mitigate these issues. However, inherent limits to pipelining exist due to structural hazards, data hazards, and increased complexity in deeper pipelines.
Exploiting Instruction-Level Parallelism
Instruction-Level Parallelism (ILP) enables processors to execute multiple instructions simultaneously, improving performance without increasing clock speed. Effective exploitation of ILP hinges on various techniques such as pipelining, superscalar architecture, and handling data and control hazards. Despite its advantages, there are inherent limitations like instruction dependency, memory latency, and power consumption that can constrain the effective utilization of ILP.
Memory
Memory is a crucial component in computer systems that enables data storage and retrieval, influencing overall system performance. The chapter discusses various types of memory, including cache, main memory, and secondary storage, alongside their hierarchies and management techniques. Insights into advanced memory technologies and challenges in memory systems highlight current trends and future developments in the field.
Caches
Caches play a crucial role in enhancing system performance by acting as a high-speed storage layer between the CPU and main memory. The chapter discusses various levels of cache, principles governing cache access, the types of cache misses, write policies, coherence issues in multi-core systems, and strategies for optimizing cache performance. It emphasizes the importance of design considerations in balancing speed, cost, and power consumption for effective cache operation.
Multicore
Multicore processors enhance computing performance and efficiency by housing multiple cores capable of simultaneous task execution. They address challenges associated with single-core processors, such as heat dissipation and power consumption, while offering benefits in parallelism and energy efficiency. These architectures present opportunities and challenges, including load balancing, memory management, and the need for software optimization to leverage multicore capabilities.
Multithreading
Multithreading is a method that allows multiple threads to run concurrently, enhancing CPU utilization and application responsiveness. Different multithreading models, including many-to-one, one-to-one, and many-to-many, demonstrate varying degrees of effectiveness in using processor resources. The chapter also discusses synchronization mechanisms vital for thread safety, and highlights challenges and techniques in multithreading, such as thread pools and work queues, across various programming languages and operating systems.
Vector, SIMD, GPUs
Vector processing is an efficient technique for handling large datasets by performing operations on multiple data elements simultaneously. This chapter explores SIMD, which enhances parallel computing capabilities in CPUs and GPUs, enabling faster processing for various applications such as graphics rendering and machine learning. Furthermore, advancements in SIMD architectures and the rise of General-Purpose GPUs (GPGPUs) have transformed computation across sectors by efficiently managing vast amounts of parallelizable tasks.