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Dataflow Analysis for Datarace-Free Programs. (ESOP ‘11) Arnab De Joint work with Deepak D’Souza and Rupesh Nasre Indian Institute of Science, Bangalore. Why Datarace-Free Programs? . Java, C++, … programs. Racy programs. Very weak guarantees. DRF programs.

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dataflow analysis for datarace free programs

Dataflow Analysis for Datarace-Free Programs

(ESOP ‘11)

Arnab De

Joint work with Deepak D’Souza and Rupesh Nasre

Indian Institute of Science, Bangalore

why datarace free programs
Why Datarace-Free Programs?

Java, C++, …

programs

Racy programs

Very weak guarantees

DRF programs

Sequentially consistent semantics

  • Dataraces are often indicators of bugs.
sc for drf
SC for DRF

Verifier

Bug/Memory model specific reasoning required

DRF?

No

Yes

Analysis for

DRF programs!

Perform

optimization

assume DRF

Optimized code

Compiler

datarace free programs
Datarace-Free Programs
  • In an execution, a release action synchronizes-with (sw)all acquire actions on same variable after it.
  • In an execution, happens-before(hb) relation is reflexive, transitive closure of synchronizes-with and program-order.
  • In all SC executions, all conflicting accesses must be ordered by happens-before.
datarace free programs5
Datarace-Free Programs

t1++;

lock l;

x = 1;

unlock l;

t2++;

lock l;

x = 2;

unlock l;

t++;

lock l;

x = 1;

unlock l;

t2++;

lock l;

x = 2;

unlock l;

sw edge

po edge

po edge

slide6

buf *p; lock l;

p = new (...);

p->data = new (...);

*p->data = VAL;

spawn (“prod”); spawn(“cons”);

cons () {

while (1) {

lock (l);

v = *p->data;

unlock (l);

}

}

prod () {

while (1) {

lock (l);

oldv = *p->data;

free (p->data);

newv = nextv (oldv);

p->data = new (...);

*p->data = newv;

unlock (l);

}

}

dataflow analysis for concurrent programs
Dataflow Analysis for Concurrent Programs
  • Kill dataflow facts conservatively.
    • More precise.
  • Track interleavings precisely.
    • More efficient.
  • Handle simple program constructs.
    • Handle modern language constructs.
  • Handle simple analyses.
    • Handle more complex analyses.
slide8

buf *p; lock l;

p = new (...);

p->data = new (...);

*p->data = VAL;

spawn (“prod”); spawn (“cons”);

p

p,p->data

p,p->data

cons () {

while (1) {

lock (l);

v = *p->data;

unlock (l);

}

}

prod () {

while (1) {

lock (l);

oldv = *p->data;

free (p->data);

newv = nextv (oldv);

p->data = new (...);

*p->data = newv;

unlock (l);

}

}

p,p->data

p,p->data

p,p->data

p,p->data

p,p->data

p,p->data

p,p->data

p

p

p,p->data

p.p->data

slide9

buf *p; lock l;

p = new (...);

p->data = new (...);

*p->data = VAL;

spawn (“prod”); spawn (“cons”);

p

p,p->data

p,p->data

cons () {

while (1) {

lock (l);

v = *p->data;

unlock (l);

}

}

prod () {

while (1) {

lock (l);

oldv = *p->data;

free (p->data);

newv = nextv (oldv);

p->data = new (...);

*p->data = newv;

unlock (l);

}

}

p,p->data

p,p->data

p,p->data

p,p->data

p,p->data

p,p->data

p,p->data

p

p

p,p->data

p.p->data

slide10

buf *p; lock l;

p = new (...);

p->data = new (...);

*p->data = VAL;

spawn (“prod”); spawn (“cons”);

p

p,p->data

p,p->data

cons () {

while (1) {

lock (l);

v = *p->data;

unlock (l);

}

}

prod () {

while (1) {

lock (l);

oldv = *p->data;

free (p->data);

unlock (l);

newv = nextv (oldv);

lock (l);

p->data = new (...);

*p->data = newv;

unlock (l);

}

}

p,p->data

p

p

p

p,p->data

p,p->data

p,p->data

p

p

p

p

p,p->data

p.p->data

our algorithm for lifting sequential analyses for concurrent programs
Our Algorithm for Lifting Sequential Analyses for Concurrent Programs
  • Build sync-CFG: add may-synchronize-edges from release to corresponding acquire instructions, if they can run in parallel.
    • From fork to first instruction of child thread.
    • From unlock to lock instructions on same lock variable.
    • From last instruction of a child thread to join instruction waiting for it.
    • May need to over-approximate the edges.
our algorithm for lifting sequential analyses for concurrent programs12
Our Algorithm for Lifting Sequential Analyses for Concurrent Programs
  • Sequential analysis on sync-CFG:
    • Consider flow function for synchronization instructions as id.
    • Construct flow equations on sync-CFG.
    • Compute least fixed point (lfp) of flow equations.
restrictions on analysis
Restrictions on Analysis
  • Value Set analysis:
    • Collects set of values for each lvalue at each program point, loses the correlation.
    • l := e :evaluate e on the input value set and update the value set of l.
    • if(e) : propagate values that can make e true to true branch, similarly for false branch.
    • Join operation is point-wise union.
    • Treats aliases conservatively.
restrictions on analysis 2
Restrictions on Analysis (2)
  • Abstractions of value set analysis:
    • A is an abstraction of VS if there are αandγsuch that α(lfp of VS) ≤ lfp of A and lfp of VS ≤ γ(lfp of A).
    • Null-pointer analysis, Interval analysis, Constant propagation, May pointer analysis…
interpreting the result
Interpreting the Result
  • We assume that the value set of an lvalue (or its abstraction) is relevant only at those program points where that lvalue is read.
    • Result of NPA is important only where the pointer is dereferenced.
    • Result of CP is important only where that variable is read.
  • Our result is sound only for relevant lvalues at a given program point.
why does it work
Why does it work?

For Value Set analysis:

  • LFP of sequential analysis over-approximates join-over-all-paths in sync-CFG.
  • It is enough to show that if an execution produces a value v for an lvalue l relevant at a program point E, then there is a path in sync-CFG that includes v in VS(l) at E.
path in sync cfg
Path in Sync-CFG

W: x = y

  • Induction over execution length.
  • W and R are related by hb.
  • hb = (po U sw)*
  • Flow functions of po edges over-approximate execution behavior.
  • Flow functions of sw edges are identity.

R: … = x

context sensitive analysis
Context-Sensitive Analysis
  • Analysis domain:
    • call string -> abstract state
  • On a call site c,
    • [s -> a] -> [sc -> a]
  • On return to call site c,
    • [sc -> a] -> [s -> a]
context sensitive analysis for concurrent programs
Context-Sensitive Analysis for Concurrent Programs
  • Use a summary component at each may-synchronize-with edge.
  • Join all the states at acquire and put in summary.
  • Join the summary with all (non-bottom) states at release.
results
Results

all derefs

actually safe

seq analysis

our analysis

sources of imprecision
Sources of Imprecision
  • Alias analysis, may happen in parallel analysis, …
  • Representation of multiple dynamic threads by a single static thread.
  • Paths in sync-CFG that do not correspond to any real execution.
slide23

foo() {

lock l;

x++;

unlock l;

}

main() {

fork(foo);

fork(foo);

}

baz() {

lock l;

x++;

unlock l;

}

bar() {

lock l;

x++;

unlock l;

}

conclusion
Conclusion
  • A dataflow analysis technique for DRF programs.
  • Defined the conditions for soundness.
  • Demonstrated scalability and precision.
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