Advanced CPU Core Configuration Analysis

Introduction and Motivation

Context

Modern processor design is fundamentally constrained by the power-performance-area (PPA) triangle. Engineers must constantly balance:

• Performance: How quickly the processor completes tasks
• Power Consumption: Energy efficiency and thermal constraints
• Die Area & Cost: Physical silicon space and manufacturing costs

One of the most significant performance enhancements in CPU design was the introduction of out-of-order (OoO) execution in the 1990s. Unlike traditional in-order processors that execute instructions sequentially as written in the program, OoO processors can execute independent instructions in a different order to maximize pipeline utilization and hide memory latencies.

Problem Statement

While OoO cores provide substantial performance benefits, they require additional hardware resources:

Project Objectives

1. Understand the performance impact of key architectural parameters
2. Identify performance bottlenecks and efficiency measures
3. Determine optimal configurations that maximize the performance-per-resource ratio
4. Provide data-driven insights for CPU design decisions

Computer Architecture Fundamentals

Out-of-Order Execution Pipeline

Modern OoO processors operate through several critical stages:

┌─────────────┐
│   FETCH     │  Retrieve instructions from memory
└────────┬────┘
         │
┌────────▼──────────┐
│    DECODE         │  Decompose instructions into micro-ops
└────────┬──────────┘
         │
┌────────▼──────────────┐
│  DISPATCH/RENAME      │  Rename registers to eliminate false dependencies
└────────┬──────────────┘
         │
┌────────▼──────────────┐
│  ISSUE & EXECUTION    │  Execute instructions out-of-order (when possible)
└────────┬──────────────┘
         │
┌────────▼──────────────┐
│  COMMIT/WRITEBACK     │  In-order commit to maintain program semantics
└───────────────────────┘

Key Architectural Components

1. Reorder Buffer (ROB)

2. Issue Width

3. Branch Prediction

Branches can stall the pipeline if not predicted accurately. We tested four predictors:

Experimental Methodology

Design Approach

We employed a factorial design methodology, systematically varying one parameter while holding others constant to isolate effects.

Benchmark: Quicksort

The project uses the Stanford Quicksort benchmark:

Performance Metrics Collected

Issue Width Variation Results

Key Observations

Speed Improvement (relative to iw_2):
iw_4: 26.7% faster  (0.015579 → 0.011584 seconds)
iw_6: 31.8% faster  (0.015579 → 0.010627 seconds)
iw_8: 34.2% faster  (0.015579 → 0.010253 seconds)

ROB Stall Events:
iw_2: 171
iw_4: 195
iw_6: 959 (5.6× increase)
iw_8: 472,321 (2,460× increase from iw_2!)

Branch Predictor Variation Results

Key Observations

ROB Size Variation Results

Key Observations

Speedup relative to r_16:
r_32:  0.4% (49% stall reduction)
r_64:  0.9% (74% stall reduction) ← Best ROI
r_128: 1.4% (95% stall reduction)
r_192: 1.6% (99% stall reduction)

Key Findings and Insights

Performance Improvement:
Issue Width (2→8):     34.2%
ROB Size (16→192):      1.6%
Predictor (2bit→LTAGE): 2.2%

ROB Stalls:           Impact:
< 200:                Negligible
200-1000:             1-2% loss
> 10,000:             Major bottleneck
> 100,000:            Catastrophic

GoodCore Recommendation

Comparative Analysis

Design Insights

Universal Design Recommendations

Conclusion

This project demonstrates a principled approach to computer architecture evaluation through systematic simulation and analysis. Key takeaways:

Project Impact

This comprehensive analysis provides:

• Data-driven design methodology for CPU architecture exploration
• Quantitative understanding of architectural trade-offs
• Practical recommendations for optimal core configurations
• Reproducible framework for future architectural studies

These findings, while demonstrated on Quicksort in gem5, provide insights that transfer to broader systems design and inform real architectural decisions made by CPU designers every day.