AccuracyAware Program Transformations Sasa Misailovic MIT CSAIL Collaborators

Accuracy-Aware Program Transformations Sasa Misailovic MIT CSAIL

Collaborators Martin Rinard, Michael Carbin, Stelios Sidiroglou, Henry Hoffmann, Deokhwan Kim, Fan Long, Daniel Roy, Zeyuan Allen Zhu, Michael Kling, Jonathan Kelner, Anant Agarwal

Trade Accuracy for Energy and Performance [Rinard ICS’ 06, OOPSLA’ 07; Misailovic, Sidiroglou, Hoffmann, Rinard ICSE’ 10; Carbin, Rinard, ISSTA’ 10; Hoffmann, Sidiroglou, Carbin, Misailovic, Agarwal, Rinard ASPLOS’ 11; Sidiroglou, Misailovic, Hoffmann, Rinard FSE’ 11; Misailovic, Roy, Rinard, SAS‘ 11; Zhu, Misailovic, Kelner, Rinard POPL’ 12; Carbin, Kim, Misailovic, Rinard PLDI’ 12; Misailovic, Sidiroglou, Rinard, RACES‘ 12; Misailovic, Kim, Rinard TECS PEC’ 13; Carbin, Kim, Misailovic, Rinard PEPM’ 13; Carbin, Misailovic, Rinard, OOPSLA ‘ 13; …]

Harness Approximate Computing How to systematically generate Automated Transformations approximate programs? How to predict accuracy of the results of approximate programs? Probabilistic Reasoning How to find the most profitable approximate programs? Explicit Search and Mathematical Optimization

Transformations Do less work • Loop perforation for (i = 0; i < n; i += 2) { …} • Sampling, Task skipping Do different kind of work • Randomized substitution Exploit Execution Environment • Unreliable operation placement r = var +. 2; • Unreliable memory regions, Lock elision

Where and when should we apply the transformations? What are the benefits and costs?

for (i = 0; i < n; i++) { …} for (i = 0; i < n; i += 2) { …} Optimization Framework • Find Candidates for Transformation • Analyze Effects of the Transformations • Navigate Tradeoff Space c cc

[Misailovic, Sidiroglou, Hoffmann, Rinard ICSE’ 10; Hoffmann, Sidiroglou, Carbin, Misailovic, Agarwal, Rinard ASPLOS’ 11; Sidiroglou, Misailovic, Hoffmann, Rinard FSE’ 11] Explicit Search Algorithm for Perforation Find Transformation Candidates: • Profile program to find time-consuming for loops Analyze the Effects of Transformation: • Performance: Compare execution times • Accuracy: Compare the quality of the results • Safety: memory safety, well formed output Navigate Tradeoff Space: • Combine multiple perforatable loops Prioritize loops by their individual performance and accuracy Greedy or Exhaustive Search with Pruning

Mean Normalized Time x 264 Cumulative Loop Scores Accuracy loss (%)

Computational Kernels: Several perforatable computations execute for the majority of the time # Perforatable Loops % Time X 264 14 > 60% Bodytrack 8 > 75% Swaptions 4 > 99% Ferret 2 > 40% Blackscholes 1 > 98% Canneal 1 > 5% Streamcluste r 1 > 98% Benchmark Computational patterns: • Distance metrics • Data Statistics • Iteration steps • Monte-Carlo

Next Step: Analyses with Guarantees Accuracy analysis: Results valid for a whole range of inputs, not just those used in testing Navigation: Explore the space of transformed programs to find those with optimal tradeoffs

Accuracy Analysis: Probabilistic Reasoning [Misailovic, Roy, Rinard SAS ’ 11; Zhu, Misailovic, Kelner, Rinard POPL ’ 12; Misailovic, Sidiroglou, Rinard, RACES ‘ 12, Misailovic, Kim, Rinard TECS PEC ’ 13, Carbin, Misailovic, Rinard, OOPSLA ’ 13, Misailovic, Rinard WACAS ‘ 14,

[Zhu, Misailovic, Kelner, Rinard POPL 2012; Misailovic, Rinard WACAS 2014] Optimization of Map-Fold Computations out. List = Map ( Func(x), input. List ) Func 0: ( 0, T 0) Func 1: ( 1, T 1) Func 2: ( 2, T 2)

[Zhu, Misailovic, Kelner, Rinard POPL 2012; Misailovic, Rinard WACAS 2014] Optimization of Map-Fold Computations Func 0: ( 0, T 0) Func 1: ( 1, T 1) Func 2: ( 2, T 2) Linear + Dynamic programming

[Misailovic, Carbin, Achour, Qi, Rinard MIT-TR 14] Chisel: Automatic Generation of Approximate Rely Programs Developer’s reliability specification float<0. 99*R(x)> f(float in unrel x) { y = g(x) +. h(x); return y *. y; } Variable and Operation Annotations

[Misailovic, Carbin, Achour, Qi, Rinard MIT-TR 14] Chisel: Automatic Generation of Approximate Rely Programs Developer’s specification Integer Linear Programming

Navigate Search Space: Mathematical Optimization [Zhu, Misailovic, Kelner, Rinard POPL ’ 12; Misailovic, Rinard WACAS ‘ 14 Misailovic, Carbin, Achour, Qi, Rinard, MIT-TR ‘ 14]

Looking Forward Fully Exploit Optimization Opportunities: both application- and hardware-level transformations Reasoning About Uncertainty: • logic-based techniques • probabilistic techniques • empirical techniques Practical Aspects: provide intuition and control for developers and domain experts

Takeaway Accuracy-aware transformations • Improve performance • Reduce energy consumption • Facilitate dynamic adaptation and software specialization Program analysis and search can help find profitable, safe, and predictable tradeoffs