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Catching Accurate Profiles in Hardware Satish Narayanasamy, Timothy Sherwood, Suleyman Sair, Brad Calder, George Varghese Presented by Jelena Trajkovic ICS 280/259

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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? => this information is used for storing compressed values in data cache Trace formation dynamically extracting and ordering frequently executed code => 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

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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% => 100 entries 0.1% => 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 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 SPEC95:go, 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)