Programming Abstractions for Approximate Computing

Report
Programming Abstractions for
Approximate Computing
Michael Carbin
with Sasa Misailovic, Hank Hoffmann,
Deokhwan Kim, Stelios Sidiroglou, Martin Rinard
MIT
Challenges for Programming
• Expression (specifying approximations)
• Reasoning (verifying resulting program)
• Debugging (reproducing failures)
• Deployment (changes in assumptions)
Challenges for Programming
• Expression (specifying approximations)
• Reasoning (verifying resulting program)
• Debugging (reproducing failures)
• Deployment (changes in assumptions)
Fundamental Questions
• Scope
• What do we approximate and how?
• Specifications of “Correctness”
• What is correctness?
• Reasoning
• How do we reason about correctness?
[Misailovic, Carbin, Achour, Qi, Rinard MIT-TR 14]
What do we approximate?
• Domains have inherent uncertainty
Benchmark
Domain
LOC
LOC
(Kernel)
Time in
Kernel (%)
sor
numerical
173
23
82.30%
blackscholes
finance
494
88
65.11%
532
62
99.20%
532
93
98.86%
218
88
93.43%
dct
idct
scale
image
processing
• Execution time dominated by a fraction of code
Approximation Transformations
• Approximate Hardware
PCMOS, Palem et al. 2005; Narayanan et al., DATE ’10; Liu et al. ASPLOS ’11;
Sampson et al, PLDI ’11; Esmaeilzadeh et al. , ASPLOS ’12, MICRO’ 12
• Function Substitution
Hoffman et al., APLOS ’11; Ansel et al., CGO ’11; Zhu et al., POPL ‘12
• Approximate Memoization
Alvarez et al., IEEE TOC ’05; Chaudhuri et al., FSE ’12; Samadi et al., ASPLOS ’14
• Relaxed Synchronization (Lock Elision)
Renganarayana et al., RACES ’12; Rinard, HotPar ‘13; Misailovic, et al., RACES ’12
• Code Perforation
Rinard, ICS ‘06; Baek et al., PLDI 10; Misailovic et al., ICSE ’10; Sidiroglou et al.,
FSE ‘11; Misailovic et al., SAS ‘11; Zhu et al., POPL ‘12; Carbin et al. PEPM ’13;
Example: Loop Perforation
float sum = 0;
for (int i = 0; i < n; i += 1) {
sum = sum + a[i];
}
float avg = sum / n;
Example: Loop Perforation
float sum = 0;
for (int i = 0; i < n; i += 1) {
sum = sum + a[i];
i += 2
}
float avg = sum / n;
float avg = (sum * 2) / n;
• Skip iterations (or truncate or random subset)
• Potentially add extrapolations to reduce bias
What is correctness?
.c
.c
Traditional
Transformation
≡
What is correctness?
.c
.c
Approximate
Transformation
What is correctness?
• Safety: satisfies standard unary assertions
(type safety, memory safety, partial functionality)
• Accuracy: program satisfies relational assertions
that constrain difference in results
Relational Assertions
• Contribution: program logic and verification system that
supports verifying relationships between implementations
relate |x<o> - x<a>| / x<o> <= .1;
• x<o>: value of x in original implementation
• x<a>: value of x in approximate implementation
[Carbin, Kim, Misailovic, Rinard PLDI’12, PEPM ‘13]
Example: Loop Perforation
float sum = 0;
for (int i = 0; i < n; i += 1) {
sum = sum + a[i];
i += 2
}
float avg = sum / n;
float avg = (sum * 2) / n;
Example: Loop Perforation
float sum = 0;
for (int i = 0; i < n; i += 1) {
sum = sum + a[i];
i += 2
}
float avg = sum / n;
float avg = (sum * 2) / n;
b-a
• Worst-case error:
2
Novel Safety Verification Concept
• Assume validity of assertions in original program
• Use relations to prove that approximation does
not change validity of assertions
p<o> == p<a> ∧ safe(*p<o>) ⊨ safe(*p<a>)
assert (safe(*p))
• Lower verification complexity than verifying an
assertion outright
Specifications and Reasoning
• Worst-case (for all inputs)
•
•
•
Non-interference [Sampson et al., PLDI ‘11]
Assertions [Carbin et al., PLDI ‘12; PEPM ‘13]
Accuracy [Carbin et al., PLDI ‘12]
• Statistical (with some probability)
•
•
•
•
Assertions [Sampson et al., PLDI ‘14]
Reliability [Carbin, Misailovic, and Rinard, OOPSLA ‘13]
Expected Error [Zhu, Misailovic et al., POPL ‘13]
Probabilistic Error Bounds [Misailovic et al., SAS ‘13]
Questions from Computer Science
Question #1:
“Programmers will never do this.”
- Unnamed Systems and PL Researchers
Reasoning
Complexity
Challenge
Partial
Specifications
Types
Expressivity
Full Functional
Correctness
Reasoning
Complexity
Challenge
Partial
Specifications
Full Functional
Correctness
Types
Expressivity
• Ordering of unary assertions still true
(improved by relational verification)
Challenge
Probabilistic Accuracy Bounds Probabilistic Assertions
Reliability
Expected Error
Reasoning
Complexity
Assertions
Expressivity
Performance/Energy Benefit
Worst-case Accuracy
Error Distributions
Example: Loop Perforation
(Misailovic et al., SAS ‘11)
float sum = 0;
for (int i = 0; i < n; i += 1) {
sum = sum + a[i];
i += 2
}
float avg = sum / n;
float avg = (sum * 2) / n;
Example: Loop Perforation
(Misailovic et al., SAS ‘11)
float sum = 0;
for (int i = 0; i < n; i += 1) {
sum = sum + a[i];
i += 2
}
float avg = sum / n;
float avg = (sum * 2) / n;
b-a
• Worst-case error:
2
0.6 *(b - a)
• Probabilistic Error Bound (.95):
n
Question #2:
“There’s no hope for building
approximate hardware.”
- Unnamed Computer Architect
Challenge
• Approx. PL community must work with
hardware community to collaborate on
models (not the PL community’s expertise)
• Approx. hardware community faces major
challenge in publishing deep architectural
changes: simulation widely panned
• Moving forward may require large joint effort
Question #3:
“I believe your techniques
are fundamentally flawed.”
- Unnamed Numerical Analyst
Challenge
• Numerical analysts have been wronged
• Programming systems have failed to provide
support for making the statements they desire
• Approximate computing community risks
repeating work done by numerical analysis
• Opportunity for collaboration
• New research opportunities
• It’s no longer just about floating-point
Broadly Accessible Motivations
• Programming with uncertainty
(uncertain operations and data)
• Programming unrealizable computation
(large scale numerical simulations)
• Useful computation from non-digital fabrics
(analog, quantum, biological, and human)
Opportunity to build reliable and resilient
computing systems built upon anything
End
Conclusion
• Adoption hinges on tradeoff between
expressivity, complexity and benefit
• Opportunity/necessity for tighter integration of
PL and hardware communities
Reasoning
Complexity
Open Challenges and Directions
Probabilistic Accuracy Bounds
Worst-case Accuracy Probabilistic Assertions
Reliability
Expected Accuracy Error Distributions
Expressivity
Problem: benefits (performance/energy) may vary
between different types of guarantees
Open Challenges and Directions
Expressivity and Reasoning Complexity
Type
Checking
Partial
Specifications
Full Functional
Correctness
Experimental Results
Benchmark
LOC
LOC (Kernel)
Time in Kernel
(%)
sor
23
173
82.30%
blackscholes
88
494
65.11%
dct
62
532
99.20%
idct
93
532
98.86%
scale
88
218
93.43%
Open Challenges Directions
• Traditional Tradeoff
Dynamic
Analysis
Type
Checking
Partial
Specifications
Full Functional
Correctness
Performance/Energy
Reasoning Complexity

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