Catching Accurate Profiles in Hardware

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Catching Accurate Profiles in Hardware
ICS 280/259

• Satish Narayanasamy, Timothy Sherwood, Suleyman
  Sair, Brad Calder, George Varghese

Presented by Jelena Trajkovic
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Outline

• Introduction Motivation
• Goal
• Related Work (Stratified Sampler)
• Interval-based Profiling for a Single Hash
  Profiler
• Experimental results
• Multiple-hash Profiler
• Experimental results

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Introduction Motivation

• SW used to gather program behavior information
• Architectural support for generating profiles at
  run-time
• HW is used to assist SW,
• dependent on on system SW (for management or
  aggregation of events)
• HW-only profiler

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Introduction Motivation (cont.)

• HW optimizations that can take advantage of info
  gathered in run-time
• Cache replacement prefetching
• identifying loads that cause majority of misses
• Value based optimization
• 50 of memory accesses are dominated by 10
  distinct values
• capture this dynamically? gt this information is
  used for storing compressed values in data cache
• Trace formation
• dynamically extracting and ordering frequently
  executed code gt I-fetch more efficient
• Multiple path execution
• find branches that are hard to predict and
  execute down multiple paths

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Goal

• The goal is to build a profiling scheme that
  satisfies following properties
• Area Efficient capacity constraints (fixed
  amount of area)
• Accurate identify important / frequent events
  and count them accurately
• Timely up-to-date information about program
  behavior
• Performance Efficiency and SW Independence
  independent of system SW support to manage
  profiles (accumulate and analyze events),
  identifying in HW

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Related Work

• SW profiling
• Binary instrumentation (ATOM by Calder et al.)
• HW counter assisted profiling
• DCPI system for Alpha Processors
• HW table based profiling
• Stratified sampling (Sastry et al.)
• Co-processor profiler
• Distill information passed from main processor
  (Ziles and Sohi)

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Profiling Events

• Profiling event combination of several variables
• instruction PC, load address, register value or
  name, cache miss
• Tuple represents event as combination of 2
  variables
• ltpc, valuegt

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Related Work Stratified Sampler

• Divides the original input stream into multiple
  streams via hashing (independently sampled)

• Table of counters
• number of occurrences of different events
• counter is selected by applying hash function on
  the input event
• incremented when event appears in the input
  stream
• on reaching threshold value, counter is reset
  and event is reported (interrupt to the OS)

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Related Work Stratified Sampler (cont.)

• To reduce aliasing and improve accuracy
• Partial tags, miss counters, state information
• Hit counters number of occurrences
• Miss counters tuple hashes to particular entry,
  but tag differs (replacement policy)
• On reaching threshold value
• Generate interrupt
• Buffered, interrupt is sent when buffer fills up
• Placed in associative counter table, passed to SW
  (via intermediate buffer)
• Accumulating information in SW (5 interrupt
  overhead)

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Interval-based Profiling for a Single Hash
Profiler

• Removing SW accumulator table
• Interval-based

• significant number of occurrences within
  interval
• reset hash-table counters after every interval
• improving accuracy - shielding
• Divide execution time into intervals
• interval length fixed number of profiling
  events (tuples)
• capture only events (candidate tuples) that occur
  more than candidate threshold ( of interval
  length)

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Single Hash Architecture

• accumulator table is fully associative and tagged
• if (input tuple is in acc. table )
• inc counter
• else
• hash into hash-table
• increment corresponding counter
• hash-table does not contain tags aliasing
• if (tuple reaches candidate threshold value)
• if (acc. table is not full)
• acc. table is allocated
• mark entry as non-replicable till the end of
  interval
• particular entry is not given as an input to the
  hash-table shielding
• if (end of the interval)
• flush hash-table
• mark all entries in acc. table as replaceable

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Single Hash Architecture (cont.)

• Calculate worst case number of entries in the
  acc. table (avoid capacity and aliasing issues)
  as a function of profile interval length and
  candidate threshold
• number of events that determine profiling
  interval
• number of occurrences in order to get recorded in
  acc. table (percentage of interval length)
• e.g. interval length 10,000
• candidate threshold 1 gt 100 entries
• 0.1 gt 1,000 entries
• 10,000 w/ 1 and 1 million w/ 0.1
• Hash-table 2K entries

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Single Hash Architecture (cont.)

• Hash functions for a given tuple ltpc, valuegt
• npc flip(randomize(pc))
• nv randomize(value)
• index xor-fold(npc xor nv, index-size)
• Optimizations
• Retaining keeps top entries in acc. table from
  the previous interval
• Resetting reset counter in hash-table, after it
  reaches candidate threshold

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Experimental setup

• SPEC95go, li, vortex SPEC2K gcc, vortex
  deltablue, sis, burg
• Compilation
• DEC Alpha 21164, DEC C (full optimizations)
• Profiling analysis ATOM
• Fast forwarded and then ran for 500 million
  instructions

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Error Calculation

• For each interval compare candidates seen by HW
  profiler and perfect profiler
• False Positive
• False Negative
• Neutral Positive
• Neutral Negatives

• Total error rate for an interval

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Experimental Results

• Accuracy of HW profiling depends
• number of unique tuples in an interval (distinct
  tuples)
• number of unique tuples that cross threshold
• Analysis of candidate tuples

Number of distinct tuples seen in an interval
on average
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• Number of unique candidate tuples in an interval
  on average

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• Percentage of variation of candidates from
• one interval to the next

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Error rates

• Single Hash table with retaining/resetting
  results across a set of benchmarks

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Multiple-hash Profiler

• Independent hash functions (for each table)
• if(no entry in acc. table)
• hash to each table
• update each counter

• if(all entries for particular tuple in hash table
  reach candidate threshold)
• add entry to the acc. table
• reset counters in hash-table (immediately or at
  the end of interval)
• Conservative update update just smallest counter

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• Muti-hash profiler for an interval of 10,000, 1
  candidate threshold, and a total number of 2K
  hash-table entries

Muti-hash profiler for an interval of 1 million,
0.1 candidate threshold, and a total number of
hash-table entries of 2K
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• Varying number of hash tables for the best
  muti-hash profiler - C1, R0 (w/ conservative
  update and w/o resetting) (10,00, 1 - L 1mill,
  0.1 - R)

Variation in the error across different intervals
(BSH w/ resetting - L multi-hash w/ conservative
update and no resetting 4hash tables - R)
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Summary

• Profiling architecture
• Efficiently filters out important data
• Efficient in terms of HW cost (6KB (1KB or 10
  KB) and overhead (no performance overhead)