Computer Architecture: CPU, Memory, and Machine Code

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Questions and Answers

In the context of computer architecture, what distinguishes a special-purpose register from a general-purpose register, and how does this distinction impact the efficiency of specific computational tasks?

General-purpose registers are faster due to their simplified design, while special-purpose registers incorporate error-checking mechanisms that introduce latency but ensure data integrity.
Special-purpose registers are designed with specific functionalities, such as pointing to the stack or storing status flags, enhancing the speed of context switching and interrupt handling compared to general-purpose registers. (correct)
Special-purpose registers have larger storage capacities, allowing them to hold more data than general-purpose registers, which is crucial for processing large datasets in memory-intensive applications.
Special-purpose registers are exclusively used for floating-point arithmetic, whereas general-purpose registers handle integer operations, leading to optimized performance in numerical computations.

How does the Stored Program Concept fundamentally alter the operational paradigm of a CPU, and what are the consequential trade-offs concerning memory management, instruction execution, and system vulnerability?

Storing programs in main memory allows for non-sequential instruction execution, optimizing performance for parallel processing but complicating debugging and program verification.
The Stored Program Concept reduces the CPU's instruction set, leading to simpler hardware design but requiring compilers to generate more complex code sequences for high-level languages.
By storing programs in main memory, the Stored Program Concept enables dynamic modification of code during runtime, enhancing flexibility but increasing the risk of security exploits through code injection. (correct)
The Stored Program Concept necessitates a larger CPU cache to store frequently used instructions, which decreases access time but increases the cost and complexity of the CPU design.

Within the context of machine language instruction design, how do opcode length and complexity influence the instruction set architecture's (ISA) power efficiency, instruction density, and overall system performance?

Longer opcodes allow for a larger variety of instructions, decreasing the number of clock cycles per operation but increasing energy consumption per instruction due to the complexity of the decoding logic.
Shorter opcodes improve instruction density, leading to better cache utilization and reduced memory access latency, but limit the total number of unique instructions the ISA can support.
Complex opcodes enable the execution of high-level language constructs directly in hardware, significantly boosting performance for compiled applications but increasing the cost of the CPU and the risk of speculative execution vulnerabilities.
Variable-length opcodes optimize both instruction density and power efficiency by encoding frequently used instructions with shorter codes, but they introduce significant complexity in the instruction decoding pipeline. (correct)

What are the primary architectural trade-offs between Reduced Instruction Set Computing (RISC) and Complex Instruction Set Computing (CISC) concerning code density, power consumption, and suitability for specialized applications like embedded systems and high-performance computing?

RISC architectures achieve lower power consumption due to their simpler instruction set, making them ideal for embedded systems, while CISC architectures offer higher code density, benefiting memory-constrained applications. (B)

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How does the strategic utilization of machine instruction types such as data transfer, arithmetic/logic, and control instructions facilitate the optimization of program execution speed, memory footprint, and energy consumption within a CPU?

Optimizing control instructions reduces branch mispredictions and pipeline stalls, improving instruction throughput but requiring more complex branch prediction hardware and compiler optimization. (C)

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In the context of program execution within a CPU, how do the instruction register and program counter interact to orchestrate the fetch-decode-execute cycle, and what mechanisms are in place to handle exceptions and interrupts that disrupt this cycle?

The program counter sequences instructions, while the instruction register holds and decodes them; interrupts and exceptions invoke a context switch, saving the program counter and instruction register states to a stack. (A)

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How can the principles of masking be applied using bitwise logic operations (AND, OR, XOR) to optimize data manipulation tasks such as selective bit modification, flag management, and data encryption within resource-constrained computing environments?

Masking with AND allows selective bit extraction, while OR enables selective bit setting; XOR can toggle bits, providing efficient flag management with minimal computational overhead suitable for resource-constrained systems. (C)

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What are the inherent challenges and trade-offs associated with employing rotation and shift operations in arithmetic and logical computations, particularly concerning data integrity, overflow handling, and performance optimization across diverse CPU architectures?

Shift operations can cause data loss due to bits shifted out of the register, but this can be mitigated through appropriate overflow detection and handling mechanisms, albeit at the cost of increased overhead. (D)

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Within the domain of computer architecture, how does memory-mapped I/O enable seamless integration of peripheral devices, and what are the implications of this approach regarding address space allocation, interrupt handling, and concurrent data access?

Devices appear as memory locations, which streamlines data access using standard load and store instructions, although it may require careful management of memory address space and synchronization mechanisms to prevent conflicts. (D)

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What are the primary advantages and disadvantages of utilizing Direct Memory Access (DMA) for data transfer between peripheral devices and main memory, specifically concerning CPU utilization, system bus contention, and data coherency management?

DMA transfers reduce CPU involvement in data transfers, improving overall system performance, but may lead to increased contention on the system bus and require careful management of data coherency to avoid inconsistencies. (B)

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How do parallel and serial communication methods differ in terms of data transfer rate, susceptibility to interference, and application suitability, and what are the key factors influencing the choice between them in embedded systems design?

Parallel communication offers higher data transfer rates but is more susceptible to interference due to the simultaneous transmission of multiple signals, making it less suitable for noisy environments compared to serial communication. (D)

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How does bandwidth, as a measure of maximum available data transfer rate, influence the performance of networked applications, and what strategies can be employed to mitigate the impact of bandwidth limitations on user experience?

Insufficient bandwidth can lead to sluggish performance; strategies such as data compression, caching, and Quality of Service (QoS) prioritization can improve user experience despite bandwidth limitations. (B)

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What are the implications of programming language abstraction on machine-level control, and how can developers reconcile the ease of high-level programming with the need for optimization and direct hardware manipulation?

Abstraction inherently isolates programmers from direct hardware manipulation, but this can be circumvented by using assembly language for critical sections, sacrificing portability for performance. (C)

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In the context of parallel processing architectures, what are the defining characteristics and application domains of Single Instruction, Single Data (SISD), Single Instruction, Multiple Data (SIMD), Multiple Instruction, Single Data (MISD), and Multiple Instruction, Multiple Data (MIMD) paradigms?

SIMD architectures excel in applications requiring identical operations on large datasets, while MIMD's flexibility is tailored for general-purpose computing with diverse, independent tasks. (C)

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Considering the complexities of modern CPU design, how do advancements such as pipelining and parallel processing interplay to enhance throughput, and what are the consequential challenges concerning data dependencies, hazard mitigation, and power management?

Pipelining allows multiple instructions to be in different stages of execution simultaneously, while parallel processing allows multiple instructions to be executed at the same time; data dependencies and hazards must be mitigated while managing power consumption. (C)

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How does the interplay between advances in computer architecture and programming languages impact the development of secure and resilient software systems, particularly in the face of emerging threats such as Spectre, Meltdown, and Rowhammer attacks?

Advances in both domains are crucial; architectural redesigns provide hardware-level defenses, while programming languages must evolve to offer safer programming paradigms and better tools for vulnerability detection and mitigation. (A)

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In the context of Python programming, how does the use of bitwise operators for data manipulation compare to other methods regarding execution speed, memory usage, and code readability, particularly in performance-critical applications?

Bitwise operators offer direct hardware control, resulting in faster execution and lower memory usage compared to higher-level constructs, but may reduce code portability and maintainability. (B)

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How can concurrency and parallelism improve data throughput, and what are the most significant trade-offs in overhead, complexity, and overhead?

Concurrency improves the CPU use percentage relative to using synchronous or blocking systems. (D)

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Regarding control structures like 'if' and 'while' statements in Python, how do they impact the overall structure of a program. If code is written poorly, how will it affect code maintainability, reliability, testability, security, and deployment?

Code that is easy to visually follow, and which has few side dependencies, will have better maintainability, reliability, testability, security and deployment. (D)

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In the realm of Python programming, what are the defining characteristics of fruitful and void functions? Are there differences in side effects, state management, and error handling?

Void functions return nothing, where fruitful functions return something. (D)

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Regarding memory-mapped I/O, how does it compare to other ways of communicating to peripherals, what is the data throughput, what are the edge cases, and what are the architectural trade-offs?

Most systems use separate hardware systems with interrupt-like signalling, because this can offer greater safety, hardware isolation, and data throughput. (A)

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Consider a hypothetical CPU architecture where the program counter (PC) is 20 bits wide and the instruction register (IR) is 32 bits wide. The system uses a variable-length instruction encoding scheme where opcodes can be 1, 2, or 3 bytes long, and operands can be immediate values, register addresses, or memory addresses. Memory is byte-addressable. What complications will arise and how can it be handled in the fetch-decode-execute cycle?

The 20-bit PC limits the addressable memory space to only 1MB, which can severely restrict program size and data storage capabilities unless techniques like segmentation or paging are implemented. (B)

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In a multithreaded application employing Direct Memory Access (DMA) to transfer data from a high-speed network interface card (NIC) to main memory, which factors most significantly influence overall throughput, and how can potential bottlenecks be addressed?

The most influential factors are the DMA transfer size, the number of threads accessing the data concurrently, and the memory bus bandwidth. (D)

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How will SIMD improve matrix multiplication, and what are the trade-offs?

SIMD has high performance and can do more computations. However, it will require hardware support that may not be available. There is no graceful degradation. (A)

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Flashcards

Computer Architecture

The fundamental architecture of a computer, including the CPU, main memory, and bus.

Central Processing Unit (CPU)

The core of the computer, responsible for executing instructions.