Project 12: Microbenchmark Cache and Branch Lab

A microbenchmark suite that measures cache effects and branch prediction behavior, and correlates results with disassembly.

Quick Reference

Attribute Value
Difficulty Level 4
Time Estimate 3-4 weeks
Main Programming Language C or C++ (with assembly inspection) (Alternatives: Rust, Go)
Alternative Programming Languages Rust, Go
Coolness Level Level 4
Business Potential 1
Prerequisites Performance, Microarchitecture, and SIMD, Performance, Microarchitecture, and SIMD
Key Topics Performance, Microarchitecture, and SIMD, Performance, Microarchitecture, and SIMD

1. Learning Objectives

By completing this project, you will:

  1. Explain why microbenchmark cache and branch lab reveals key x86-64 behaviors.
  2. Build a deterministic tool with clear, inspectable output.
  3. Validate correctness against a golden reference output.
  4. Connect the tool output to ABI and architecture rules.
  5. It connects assembly-level code to real performance behavior.

2. All Theory Needed (Per-Concept Breakdown)

Performance, Microarchitecture, and SIMD

Fundamentals Performance on x86-64 is shaped by microarchitecture: pipelines, caches, branch prediction, and execution ports. The ISA defines what is correct, but microarchitecture determines how fast it runs. SIMD instructions operate on vectors in XMM/YMM/ZMM registers to process multiple data elements in parallel. The ABI and compiler conventions influence whether values are kept in registers or spilled to memory, which directly impacts performance. Understanding latency vs throughput and cache behavior is essential for reasoning about assembly-level performance. (Sources: Intel SDM, Microsoft x64 architecture docs)

Deep Dive Microarchitecture is the hidden layer between instructions and performance. Modern x86-64 CPUs decode instructions into micro-operations, schedule them across multiple execution units, and reorder them to maximize throughput while preserving architectural correctness. The result is that two instruction sequences with identical semantics can have very different performance characteristics. This is why assembly-level performance work is about dependency chains, not just counting instructions.

Pipeline stages include fetch, decode, dispatch, execute, and retire. Stalls occur when the pipeline cannot make progress due to data hazards or resource conflicts. Data hazards arise when an instruction depends on the result of a previous instruction that has not completed. This creates a dependency chain that determines latency. Throughput is how many independent operations can be completed per cycle when there are no dependencies. Good performance comes from breaking dependency chains and keeping execution units busy.

Cache behavior is often the dominant factor. The memory hierarchy includes L1, L2, and L3 caches, each with different latencies. If a load misses the cache, it can stall the pipeline for many cycles. This is why data layout and access patterns are critical. Assembly-level optimizations often focus on improving locality, aligning data, and prefetching. Misaligned accesses may be allowed but can cause extra microarchitectural work.

Branch prediction is another major factor. Conditional branches that are hard to predict cause pipeline flushes, which wastes cycles. Assembly programmers sometimes use conditional move or predicated instructions to avoid unpredictable branches. However, these trade off branch penalties for extra execution work, so the right choice depends on workload characteristics.

SIMD expands the architectural state with vector registers and instructions that operate on multiple elements at once. SSE and AVX provide 128-bit and 256-bit registers, and AVX-512 provides 512-bit. The ABI defines how these registers are used for passing floating-point and vector arguments. SIMD is powerful but introduces alignment and instruction selection constraints. For example, some vector loads require alignment, and some operations have different latency/throughput characteristics. Understanding these details helps you interpret compiler-generated vector code and write hand-optimized sequences when necessary.

Performance measurement requires careful methodology. Microbenchmarks must isolate the instruction sequence of interest, warm up caches, and avoid noise from frequency scaling or OS scheduling. Tools like perf or VTune can help, but you should also be able to reason from first principles: count dependencies, identify loads that may miss in cache, and understand how the CPU might reorder operations.

Ultimately, performance at the assembly level is a negotiation between the ISA contract and the microarchitecture’s capabilities. You will learn to interpret disassembly not just as a functional artifact but as a performance story: why the compiler arranged operations in a certain order, which registers are reused, and where stalls might occur.

How this fits on projects

  • Project 11 explores SIMD and vector semantics.
  • Project 12 focuses on microbenchmarks, cache behavior, and branch prediction.

Definitions & key terms

  • Latency: Time for a dependent operation to complete.
  • Throughput: Rate of operations per cycle when independent.
  • Cache miss: Access that must fetch from a slower memory level.
  • Branch prediction: CPU guess of branch direction.
  • SIMD: Single Instruction, Multiple Data processing.

Mental model diagram

INSTRUCTION STREAM
  |
  v
DECODE -> uOPS -> SCHEDULER -> EXEC UNITS -> RETIRE
                 ^      |
                 |      v
               CACHES  MEMORY

How it works

  1. Instructions decode into micro-operations.
  2. Scheduler issues independent uops to execution units.
  3. Data dependencies determine latency chains.
  4. Cache misses stall dependent operations.
  5. Branch mispredicts flush the pipeline.

Invariants and failure modes:

  • Invariant: Architectural state updates in order at retirement.
  • Failure: Mispredicted branches waste cycles and reduce throughput.
  • Invariant: SIMD registers require proper data alignment assumptions.
  • Failure: Misaligned vector operations can cause slowdowns or faults.

Minimal concrete example (pseudo-assembly, not real code)

# PSEUDOCODE ONLY
VEC_LOAD  VREG0, [PTR]
VEC_ADD   VREG0, VREG1
VEC_STORE [PTR], VREG0

Common misconceptions

  • “More instructions always means slower.” Dependency chains matter more.
  • “SIMD is always faster.” It depends on data layout and alignment.
  • “Cache is just memory.” Cache behavior dominates performance.

Check-your-understanding questions

  1. Why can a small number of dependent instructions be slower than many independent ones?
  2. What causes a pipeline flush?
  3. Why does alignment matter for SIMD?

Check-your-understanding answers

  1. Dependencies create latency chains that block parallelism.
  2. Branch misprediction or exceptions cause flushes.
  3. Misalignment can force extra micro-ops or penalties.

Real-world applications

  • Performance tuning and optimization
  • SIMD-heavy workloads (multimedia, ML kernels)
  • Profiling and bottleneck analysis

Where you will apply it Projects 11, 12

References

  • Intel 64 and IA-32 Architectures Software Developer’s Manual (Intel)
  • “Computer Architecture” by Hennessy and Patterson - Ch. 2, 3, 5
  • “Inside the Machine” by Jon Stokes - Ch. 4-6

Key insights Performance is an emergent property of dependencies, caches, and prediction.

Summary You optimize assembly by understanding how the CPU actually executes it, not just what it means.

Homework/Exercises to practice the concept

  • Sketch the dependency graph of a simple arithmetic chain.
  • Predict where cache misses might occur in a loop over a large array.

Solutions to the homework/exercises

  • Dependencies form a linear chain; parallelize by splitting independent ops.
  • Misses occur when the array exceeds cache size and lacks locality.

    Performance, Microarchitecture, and SIMD

Fundamentals Performance on x86-64 is shaped by microarchitecture: pipelines, caches, branch prediction, and execution ports. The ISA defines what is correct, but microarchitecture determines how fast it runs. SIMD instructions operate on vectors in XMM/YMM/ZMM registers to process multiple data elements in parallel. The ABI and compiler conventions influence whether values are kept in registers or spilled to memory, which directly impacts performance. Understanding latency vs throughput and cache behavior is essential for reasoning about assembly-level performance. (Sources: Intel SDM, Microsoft x64 architecture docs)

Deep Dive Microarchitecture is the hidden layer between instructions and performance. Modern x86-64 CPUs decode instructions into micro-operations, schedule them across multiple execution units, and reorder them to maximize throughput while preserving architectural correctness. The result is that two instruction sequences with identical semantics can have very different performance characteristics. This is why assembly-level performance work is about dependency chains, not just counting instructions.

Pipeline stages include fetch, decode, dispatch, execute, and retire. Stalls occur when the pipeline cannot make progress due to data hazards or resource conflicts. Data hazards arise when an instruction depends on the result of a previous instruction that has not completed. This creates a dependency chain that determines latency. Throughput is how many independent operations can be completed per cycle when there are no dependencies. Good performance comes from breaking dependency chains and keeping execution units busy.

Cache behavior is often the dominant factor. The memory hierarchy includes L1, L2, and L3 caches, each with different latencies. If a load misses the cache, it can stall the pipeline for many cycles. This is why data layout and access patterns are critical. Assembly-level optimizations often focus on improving locality, aligning data, and prefetching. Misaligned accesses may be allowed but can cause extra microarchitectural work.

Branch prediction is another major factor. Conditional branches that are hard to predict cause pipeline flushes, which wastes cycles. Assembly programmers sometimes use conditional move or predicated instructions to avoid unpredictable branches. However, these trade off branch penalties for extra execution work, so the right choice depends on workload characteristics.

SIMD expands the architectural state with vector registers and instructions that operate on multiple elements at once. SSE and AVX provide 128-bit and 256-bit registers, and AVX-512 provides 512-bit. The ABI defines how these registers are used for passing floating-point and vector arguments. SIMD is powerful but introduces alignment and instruction selection constraints. For example, some vector loads require alignment, and some operations have different latency/throughput characteristics. Understanding these details helps you interpret compiler-generated vector code and write hand-optimized sequences when necessary.

Performance measurement requires careful methodology. Microbenchmarks must isolate the instruction sequence of interest, warm up caches, and avoid noise from frequency scaling or OS scheduling. Tools like perf or VTune can help, but you should also be able to reason from first principles: count dependencies, identify loads that may miss in cache, and understand how the CPU might reorder operations.

Ultimately, performance at the assembly level is a negotiation between the ISA contract and the microarchitecture’s capabilities. You will learn to interpret disassembly not just as a functional artifact but as a performance story: why the compiler arranged operations in a certain order, which registers are reused, and where stalls might occur.

How this fits on projects

  • Project 11 explores SIMD and vector semantics.
  • Project 12 focuses on microbenchmarks, cache behavior, and branch prediction.

Definitions & key terms

  • Latency: Time for a dependent operation to complete.
  • Throughput: Rate of operations per cycle when independent.
  • Cache miss: Access that must fetch from a slower memory level.
  • Branch prediction: CPU guess of branch direction.
  • SIMD: Single Instruction, Multiple Data processing.

Mental model diagram

INSTRUCTION STREAM
  |
  v
DECODE -> uOPS -> SCHEDULER -> EXEC UNITS -> RETIRE
                 ^      |
                 |      v
               CACHES  MEMORY

How it works

  1. Instructions decode into micro-operations.
  2. Scheduler issues independent uops to execution units.
  3. Data dependencies determine latency chains.
  4. Cache misses stall dependent operations.
  5. Branch mispredicts flush the pipeline.

Invariants and failure modes:

  • Invariant: Architectural state updates in order at retirement.
  • Failure: Mispredicted branches waste cycles and reduce throughput.
  • Invariant: SIMD registers require proper data alignment assumptions.
  • Failure: Misaligned vector operations can cause slowdowns or faults.

Minimal concrete example (pseudo-assembly, not real code)

# PSEUDOCODE ONLY
VEC_LOAD  VREG0, [PTR]
VEC_ADD   VREG0, VREG1
VEC_STORE [PTR], VREG0

Common misconceptions

  • “More instructions always means slower.” Dependency chains matter more.
  • “SIMD is always faster.” It depends on data layout and alignment.
  • “Cache is just memory.” Cache behavior dominates performance.

Check-your-understanding questions

  1. Why can a small number of dependent instructions be slower than many independent ones?
  2. What causes a pipeline flush?
  3. Why does alignment matter for SIMD?

Check-your-understanding answers

  1. Dependencies create latency chains that block parallelism.
  2. Branch misprediction or exceptions cause flushes.
  3. Misalignment can force extra micro-ops or penalties.

Real-world applications

  • Performance tuning and optimization
  • SIMD-heavy workloads (multimedia, ML kernels)
  • Profiling and bottleneck analysis

Where you will apply it Projects 11, 12

References

  • Intel 64 and IA-32 Architectures Software Developer’s Manual (Intel)
  • “Computer Architecture” by Hennessy and Patterson - Ch. 2, 3, 5
  • “Inside the Machine” by Jon Stokes - Ch. 4-6

Key insights Performance is an emergent property of dependencies, caches, and prediction.

Summary You optimize assembly by understanding how the CPU actually executes it, not just what it means.

Homework/Exercises to practice the concept

  • Sketch the dependency graph of a simple arithmetic chain.
  • Predict where cache misses might occur in a loop over a large array.

Solutions to the homework/exercises

  • Dependencies form a linear chain; parallelize by splitting independent ops.
  • Misses occur when the array exceeds cache size and lacks locality.

3. Project Specification

3.1 What You Will Build

A microbenchmark suite that measures cache effects and branch prediction behavior, and correlates results with disassembly.

Why this teaches x86-64: It connects assembly-level code to real performance behavior.

Included:

  • Deterministic CLI output for a fixed input
  • Clear mapping between inputs and architectural meaning
  • A small test suite with edge cases

Excluded:

  • Full compiler or full disassembler coverage
  • Production-grade UI or packaging

3.2 Functional Requirements

  1. Deterministic Output: Same input yields identical output.
  2. Architecture-Aware: Output references ABI/ISA rules where relevant.
  3. Validation Mode: Provide a compare mode against a golden output.

3.3 Non-Functional Requirements

  • Performance: Fast enough for small inputs and interactive use.
  • Reliability: Handles malformed inputs with clear errors.
  • Usability: Outputs are readable and documented.

3.4 Example Usage / Output

$ x64bench --case cache_stride

CASE: cache_stride
STRIDE=64 bytes
ITER=10000000
RESULT: 1.35 ns/iter (L1 hit)

CASE: cache_stride
STRIDE=4096 bytes
ITER=10000000
RESULT: 12.8 ns/iter (L3/memory)

3.5 Data Formats / Schemas / Protocols

  • Input format: line-oriented text or hex bytes (documented in README)
  • Output format: stable, human-readable report with labeled fields

3.6 Edge Cases

  • Empty input or missing fields
  • Invalid numeric values or malformed hex
  • Inputs that exercise maximum/minimum bounds

3.7 Real World Outcome

This section is your golden reference. Match it exactly.

3.7.1 How to Run (Copy/Paste)

  • Build: (if needed) make or equivalent
  • Run: P12-microbenchmark-cache-branch-lab with sample input
  • Working directory: project root

3.7.2 Golden Path Demo (Deterministic)

Run with the provided demo input and confirm output matches the transcript.

3.7.3 If CLI: exact terminal transcript

$ x64bench --case cache_stride

CASE: cache_stride
STRIDE=64 bytes
ITER=10000000
RESULT: 1.35 ns/iter (L1 hit)

CASE: cache_stride
STRIDE=4096 bytes
ITER=10000000
RESULT: 12.8 ns/iter (L3/memory)

4. Solution Architecture

4.1 High-Level Design

INPUT -> PARSER -> MODEL -> RENDERER -> REPORT

4.2 Key Components

Component Responsibility Key Decisions
Parser Turn input into structured records Strict vs permissive parsing
Model Apply ISA/ABI rules Deterministic state transitions
Renderer Produce readable output Stable formatting

4.4 Data Structures (No Full Code)

  • Record: holds one instruction/event with decoded fields
  • State: represents register/flag or address state
  • Report: list of formatted output lines

4.4 Algorithm Overview

Key Algorithm: Parse and Evaluate

  1. Parse input into records.
  2. Apply rules to update state.
  3. Render the state and summary output.

Complexity Analysis:

  • Time: O(n) over input records
  • Space: O(n) for report output

5. Implementation Guide

5.1 Development Environment Setup

# Ensure basic tools are installed
# build-essential or clang, plus objdump/readelf if needed

5.2 Project Structure

project-root/
├── src/
│   ├── main.*
│   ├── parser.*
│   └── model.*
├── tests/
│   └── test_cases.*
└── README.md

5.3 The Core Question You’re Answering

How do cache behavior and branch prediction show up in real timing results?

5.4 Concepts You Must Understand First

  1. Cache hierarchy
    • Why does stride affect cache hits?
    • Book Reference: “Computer Architecture” (Hennessy, Patterson) - Ch. 5
  2. Branch prediction
    • What causes mispredict penalties?
    • Book Reference: “Inside the Machine” by Jon Stokes - Ch. 4-6

5.5 Questions to Guide Your Design

  1. Measurement strategy
    • How will you reduce noise (warmup, pinning, repeat)?
    • How will you report confidence intervals?
  2. Disassembly analysis
    • How will you map results to the instruction sequence?
    • Which counters or metrics will you collect?

5.6 Thinking Exercise

Predict the Curve

Sketch the expected latency curve as array size grows beyond L1, L2, and L3 caches.

Questions to answer:

  • Where should the knee points appear?
  • What would a branch-mispredict curve look like?

5.7 The Interview Questions They’ll Ask

  1. “Why does memory access latency jump with stride?”
  2. “How does branch prediction affect tight loops?”
  3. “What is the difference between latency and throughput?”
  4. “How do you design a reliable microbenchmark?”
  5. “Why is assembly inspection useful in performance work?”

5.8 Hints in Layers

Hint 1: Starting Point Start with a simple loop and measure time per iteration.

Hint 2: Next Level Vary stride length and measure latency changes.

Hint 3: Technical Details Pin the process to a core and disable frequency scaling if possible.

Hint 4: Tools/Debugging Use perf stat to gather branch and cache miss counters.

5.9 Books That Will Help

Topic Book Chapter
Caches “Computer Architecture” (Hennessy, Patterson) Ch. 5
Pipelines “Inside the Machine” by Jon Stokes Ch. 4-6

5.10 Implementation Phases

Phase 1: Foundation (2-3 days)

Goals:

  • Parse input format
  • Produce a minimal output Tasks:
    1. Define input grammar and example files.
    2. Implement a minimal parser and renderer. Checkpoint: Golden output matches a small input.

Phase 2: Core Functionality (1 week)

Goals:

  • Implement full rule set
  • Add validation and errors Tasks:
    1. Implement rule engine for core cases.
    2. Add error handling for invalid inputs. Checkpoint: All core tests pass.

Phase 3: Polish & Edge Cases (2-3 days)

Goals:

  • Add edge-case coverage
  • Improve output readability Tasks:
    1. Add edge-case tests.
    2. Refine output formatting and summary. Checkpoint: Output matches golden transcript for all cases.

5.11 Key Implementation Decisions

Decision Options Recommendation Rationale
Input format Text, JSON Text Easiest to audit and diff
Output format Plain text, JSON Plain text Matches CLI tooling

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit Tests Validate parsing and rule application Valid/invalid inputs
Integration Tests End-to-end output comparison Golden transcripts
Edge Case Tests Stress unusual inputs Empty input, max values

6.2 Critical Test Cases

  1. Minimal Input: One record, verify output.
  2. Boundary Values: Largest/smallest values.
  3. Malformed Input: Ensure clean error messages.

6.3 Test Data

INPUT: sample_min.txt
EXPECTED: matches golden transcript

7. Common Pitfalls & Debugging

7.1 Frequent Mistakes

Pitfall Symptom Solution
Wrong assumptions Output mismatches Re-read ABI/ISA rules
Off-by-one parsing Missing fields Add explicit length checks
Ambiguous output Hard to verify Add labels and separators

Project-specific pitfalls

Problem 1: “Results are noisy and inconsistent”

  • Why: OS scheduling and CPU frequency changes.
  • Fix: Pin to a core and repeat runs with warmups.
  • Quick test: Compare variance across 10 repeated runs.

7.2 Debugging Strategies

  • Golden diffing: Use diff to compare outputs line by line.
  • State logging: Print intermediate state after each step.

7.3 Performance Traps

  • Avoid over-optimizing; correctness and determinism matter most.

8. Extensions & Challenges

8.1 Beginner Extensions

  • Add a new input case and golden output
  • Add a summary line with counts

8.2 Intermediate Extensions

  • Add JSON output mode
  • Add validation warnings for suspicious inputs

8.3 Advanced Extensions

  • Support additional ABI or instruction variants
  • Integrate with a real binary to collect inputs

9. Real-World Connections

9.1 Industry Applications

  • Profilers and tracers: Use similar decoding and state models.
  • Security analysis: Use precise ABI knowledge to interpret crashes.
  • objdump: reference tool for binary inspection.
  • llvm-objdump: LLVM-based disassembly and inspection.

9.3 Interview Relevance

  • ABI and calling conventions are common systems interview topics.
  • Explaining decoding and linking demonstrates low-level fluency.

10. Resources

10.1 Essential Reading

  • Intel 64 and IA-32 Architectures Software Developer’s Manual - ISA reference
  • System V AMD64 ABI Draft 0.99.7 - calling convention rules

10.2 Video Resources

  • Vendor and university lectures on x86-64 and ABIs (search official channels)