Compiler-directed Data Partitioning for Multicluster Processors

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Compiler-directed Data Partitioning for
Multicluster Processors

• Michael Chu and Scott Mahlke
• Advanced Computer Architecture Lab
• University of Michigan
• March 28, 2006

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Multicluster Architectures

• Addresses the register file bottleneck
• Decentralizes architecture
• Compilation focuses on partitioning operations
• Most previous work assumes a unified memory

Register File
Data Memory
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Problem Partitioning of Data
int x100
struct foo
int y100

• Determine object placement into data memories
• Limited by
• Memory sizes/capacities
• Computation operations related to data
• Partitioning relevant to caches and scratchpad
  memories

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Architectural Model

• This work focuses on use of scratchpad-like
  static local memories
• Each cluster has one local memory
• Each object placed in one specific memory
• Data object available in the memory throughout
  the lifetime of the program

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Data Unaware Partitioning
Lose average 30 performance by ignoring data
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Our Objective

• Goal Produce efficient code
• Strategy
• Partition both data objects and computation
  operations
• Balance memory size across clusters
• Improve memory bandwidth
• Maximize parallelism

int y 100
int x100
struct foo
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First Try Greedy Approach
Data Partition Computation Partition
Data Unaware None, Profile-based placement Region-view
Greedy Region-view Greedy Profile-based Region-view

• Computation-centric partition of data
• Place data where computation references it most
  often
• Greedy approach
• Pass 1 Region-view computation partition Greedy
  data cluster assignment
• Pass 2 Region-view computation repartition
  Full knowledge of data location

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Greedy Approach Results

• 2 Clusters
• One Integer, Float, Memory, Branch unit per
  cluster
• Relative to a unified, dual-ported memory
• Improvement over Data Unaware, still room for
  improvement

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Second Try Global Data Partition

• Data-centric partition of computation
• Hierarchical technique
• Pass 1 Global-view for data
• Consider memory relationships throughout program
• Locks memory operations to clusters
• Pass 2 Region-view for computation
• Partition computation based on data location

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Pass 1 Global Data Partitioning

• Determine memory relationships
• Pointer analysis profiling of memory
• Build program-level graph representation of all
  operations
• Perform data object memory operation merging
• Respect correctness constraints of the program

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Global Data Graph Representation

• Nodes Operations, either memory or non-memory
• Memory operations loads, stores, malloc
  callsites
• Edges Data flow between operations
• Node weight Data object size
• Sum of data sizes forreferenced objects
• Object size determined by
• Globals/locals pointer analysis
• Malloc callsites memory profile

int x100
struct foo
malloc site 1
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Global Data Partitioning Example
BB1
2 Objects referenced 80 Kb
BB2
2 Objects referenced 200 Kb
1 Object referenced 100 Kb
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Pass 2 Computation Partitioning

• ObservationGlobal-level data partition is only
  half the answer
• Doesnt account for operation resource usage
• Doesnt consider code scheduling regions
• Second pass of partitioning on each scheduling
  region
• Memory operations from first phase locked in
  place

BB1
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Experimental Methodology

• Compared to
• 2 Clusters
• One Integer, Float, Memory, Branch unit per
  cluster
• All results relative to a unified, dual-ported
  memory

Data Partitioning Computation Partition
Global Global-view Data-centric Know data location
Greedy Region-view Greedy computation-centric Know data location
Data Unaware None, assume unified memory Assume unified memory
Unified Memory N/A Unified memory
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Performance 1-cycle Remote Access
Unified Memory
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Performance 10-cycle Remote Access
Unified Memory
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Case Study rawcaudio
X
Global Data Partition
Greedy Profile-based
X
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Summary

• Global Data Partitioning
• Data placement first-order design principle
• Global data-centric partition of computation
• Phased ordered approach
• Global-view for decisions on data
• Region-view for decisions on computation
• Achieves 96 performance of a unified memory on
  partitioned memories
• Future work apply to cache memories

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Data Partitioning for Multicores

• Adapt global data partitioning for cache memory
  domain
• Similar goals
• Increase data bandwidth
• Maximize parallel computation
• Different goals
• Reducing coherence traffic
• Keep working set cache size

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Questions?

• http//cccp.eecs.umich.edu

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Backup
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Future Work Cache Memories

• Adapt global data partitioning for cache memory
  domain
• Similar goals
• Increase data bandwidth
• Maximize parallelcomputation
• Different goals
• Reducing coherence traffic
• Balancing working set

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Memory Operation Merging

• Interprocedural pointer analysis determines
  memory relationships

int x int foo 100 int bar 100 void
main() int a malloc() int b int
c if(cond) c foo1 b a
else c bar1 b bar1 b
100 foo0 c
malloc
load bar
load foo
store malloc or bar
store foo
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Multicluster Compilation

• Previous techniques focused on operation
  partitioning cite some papers
• Ignores the issue of data object placement in
  memory
• Assumes shared memory accessible from each cluster

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Phase 2 Computation Partitioning

• Observation Global-level data partition is only
  half the solution
• Doesnt properly account for resource usage
  details
• Doesnt consider code scheduling regions
• Second pass of partitioning is done locally on
  each basic block of the program
• Memory operations locked into specific clusters
• Uses Region-based Hierarchical Operation
  Partitioner (RHOP)

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Computation Partitioning Example

• Memory operations from first phase locked in
  place
• RHOP performs a detailed resource-cognizant
  computation partition
• Modified multi-level Kernighan-Lin algorithm
  using schedule estimates

BB1


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