CMP Design Choices

1
CMP Design Choices

• Finding Parameters that
• Impact CMP Performance
• Sam Koblenski and Peter McClone

2
Outline

• Introduction
• Assumptions
• Plackett Burman Analysis
• Simulation methods
• Statistical Design
• Plackett Burman Results
• Mean Value Analysis
• MVA Implementation
• MVA Results
• AMVA Implementation
• AMVA Results
• Complementary Results
• Conclusions

3
Introduction

• 2 part study
• Design space is huge, how can we reduce it?
• Method 1
• Plackett Burman (PB) Analysis finds critical
  parameters
• Design uses extreme values of parameters
• Detailed architecture design can focus on a few
  parameters

4
Introduction (cont.)

• Method 2
• Mean Value Analysis Model of a CMP
• Simply designed to compute throughput
• Design choices can be narrowed down quickly
• Intuition is gained and patterns/parameter
  relationships identified

5
Assumptions - PB Design

• In-Order approximated as OoO with small window
• Die Size 300 mm2 (16 MB Cache _at_ 65nm)
• L2 Cache Size expanded to fill the die
• Discrete sizes 4, 8, 12 MB
• Associativity can be non-power-of-2
• Core size measured in Cache Byte Equivalents

6
Simulation Methodology

• Simics with Ruby Opal
• 16P sims used cache warmup files
• 2P sims ran for more transactions
• Attempted OLTP and JBB benchmarks

7
Plackett Burman Design

• Motivation
• Narrow a huge design space
• Minimize simulation runs (experiments)
• Preliminaries
• Performance Measure
• Extreme Parameter Values
• Number of Parameters (N lt 4Xn-1)

8
PB Design Example
9
PB Design Parameter Values
10
PB Results

• Extreme Values stressed the simulator
• Have not completed an entire set of runs, yet
• Possibly necessary to build a custom L2 network
  for each run

11
PB Results for JBB
12
Assumptions - MVA

• Distribution of time between memory requests is
  exponential
• Processor cores exhibit the same average behavior
  with respect to their service times and miss
  rates.
• Doubling the size of the cache reduces the miss
  rate by a factor of 1/v2
• An inorder core takes approximately the same area
  as 50 KB of cache

13
MVA Design

• Simple Closed Model

14
MVA Design

• Two phases of this Model design
• First Use the exact MVA equations
• Use average time between memory access as an
  application parameter
• Solve for throughput
• Second Use Approximate MVA (AMVA)
• Use an iterative method to converge on this
  service time
• Solve for throughput 

15
Exact MVA

• To solve for the MVA equations, we determine the
  mean residence time at all service centers
• Rp processor/L1 residence time
• RL2 L2 residence time
• RM memory residence time.
• The case with one core is trivial. Use this case
  to solve for additional cores
• Rn,p Dp (1 Qn-1,p)

16
Exact MVA results

• Using data from simulation runs throughput was
  calculated
• Miss rates, number of memory requests
• Results are erratic
• Not consistent with simulation results
• Source of the problem is most likely processor
  service time!

17
Approximate MVA Design

• An iterative method can be used to converge on a
  service time
• Uses total R as an input parameter
• Iterative method works well with approximate MVA
• Goal is to match total average residence time of
  a memory request

18
Approximate MVA Results

• Convergence using the AMVA equations does not
  always occur
• Total measured residence time cannot be reached
  with this model and parameter set.
• Variation of input values without convergence
  implies flaws in the model structure
• There is a complex relationship between the
  memory system and the rate at which a core issues
  requests that must be modeled 

19
Complementary Results

• Initial goal to produce PB Results to find
  parameters to focus on for MVA Model
• Results from both approaches could cross-verify
  correctness

20
Conclusions

• Simics has a STEEP learning curve
• lt5 weeks is not enough time for valid/any results
• Refinement of a PB Design leads to long lead
  times on valid results
• CMPs complicate the relationship between cores
  and memory subsystem
• Design methodologies that focus simulation runs
  are necessary
• More results and conclusions to follow

21
Questions

• Questions?