CH16-COA10e

Superscalar Architecture

Definition and Overview

  • Superscalar: A term first introduced in 1987, referring to advanced computer architectures designed to significantly improve performance by simultaneously executing multiple scalar instructions. Scalar instructions work primarily on single data items, known as scalar quantities.

  • Superscalar architecture marks a pivotal advancement in general-purpose processor design, allowing multiple instructions to be executed concurrently across distinct pipelines. This methodology enhances resource utilization, reduces execution time, and improves the overall throughput of instruction processing, thereby pushing the boundaries of instruction-level parallelism (ILP).

Scalar vs. Superscalar Organization

Basic Concepts

  • Scalar Organization: Typically characterized by a straightforward pipeline structure where operations—both integer and floating-point—are sequentially processed using individual functional units dedicated to each type of operation. This leads to limitations in processing speed since each instruction must wait for the previous one to complete.

  • Superscalar Organization: In contrast, it incorporates several pipelined functional units catering to both integers and floating points, enabling multiple instructions to be executed simultaneously. This architectural strategy facilitates a more efficient handling of instructions, thereby increasing throughput significantly.

Visual Comparison

  • In a scalar organization, there is one of each functional unit, which restricts parallel execution. Conversely, a superscalar organization utilizes multiple functional units, thereby enabling high levels of instruction throughput and significantly enhancing execution speed.

Speedup Factors in Superscalar Processors

  • Various studies have reported the performance speedups achievable through superscalar architectures, reinforcing their effectiveness:

    • 1.8 (TJAD70)

    • 1.8 (KUCK77)

    • 1.58 (WEIS84)

    • 2.7 (ACOS86)

    • 1.8 (SOHI90)

    • 2.3 (SMIT89)

    • 2.2 (JOUP89b)

These studies indicate substantial potential for performance enhancement via the exploitation of instruction-level parallelism, demonstrating that superscalar architectures can drastically increase the number of instructions executed per cycle.

Instruction-Level Parallelism (ILP)

  • ILP refers to the measure of how many program instructions can be executed simultaneously, influenced by compiler optimizations and the processor's hardware capabilities.

Dependencies Affecting ILP

  • True Data Dependency: Occurs when one instruction depends on the result of another.

  • Procedural Dependency: Constraints arise from the inherent sequence requirements of code execution.

  • Resource Conflicts: Happen when multiple instructions contend to access the same execution resources at once.

  • Output Dependency and Antidependency: Issues that arise due to overwriting data in registers, hindering the parallel execution of certain instructions.

Instruction Issue Policies

  • Instruction issue policies dictate the methodology for fetching instructions, executing them, and updating registers/memory. These can be categorized into three types:

    1. In-order issue with in-order completion: Instructions are fetched and completed in the same order.

    2. In-order issue with out-of-order completion: Instructions are fetched in order but can complete out of order.

    3. Out-of-order issue with out-of-order completion: Instructions can both be fetched and completed out of order, greatly enhancing throughput.

  • These mechanisms allow processors to execute multiple instructions independently, employing out-of-order processing to maximize execution efficiency.

Register Renaming

  • This technique addresses register dependencies by dynamically mapping architectural registers to physical registers, effectively reducing hazards due to antidependency and output dependency. This mechanism is crucial for allowing parallel execution of instructions without stalls due to conflicts over register usage.

Branch Prediction Mechanisms

  • Branch prediction is vital in boosting performance within pipelined architectures. Techniques such as the Branch Target Buffer (BTB) facilitate the pre-fetching of probable instructions based on historical execution patterns.

Key Approaches Include:

  • Conditional Branches

  • Indirect Branches

  • Direct Branches

  • Call and Return branches

Intel Core Microarchitecture

  • This architecture features multiple execution units that adeptly manage complex instruction pipelines through dedicated units for tasks such as instruction fetching, decoding, renaming, and out-of-order execution.

  • It implements various strategies to optimize instruction flow, including effective branch prediction and out-of-order completion, thus greatly enhancing overall processor efficiency.

ARM Cortex Architecture

  • The Cortex-A8 and Cortex-M3 processors represent cutting-edge examples of modern superscalar designs, equipped with efficient instruction fetching pipelines and executing capabilities.

  • These architectures showcase specialized adaptations to address branch prediction and instruction sequencing, maintaining robust performance in spite of potential mispredictions and data hazards, effectively ensuring seamless execution even in demanding environments.