Floating-Point Correctness Analysis at the Binary Level

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Floating Point Analysis
Using Dyninst
Mike Lam
University of Maryland, College Park
Jeff Hollingsworth, Advisor
Background
• Floating point represents real numbers as (± sgnf × 2exp)
o Sign bit
o Exponent
o Significand (“mantissa” or “fraction”)
32
16
8
4
0
IEEE Single
Exponent (8 bits)
64
32
Significand (23 bits)
16
8
4
0
IEEE Double
Exponent (11 bits)
Significand (52 bits)
• Finite precision
o Single-precision: 24 bits (~7 decimal digits)
o Double-precision: 53 bits (~16 decimal digits)
2
Motivation
• Finite precision causes round-off error
o Compromises certain calculations
o Hard to detect and diagnose
• Increasingly important as HPC scales
o Computation on streaming processors is faster in single precision
o Data movement in double precision is a bottleneck
o Need to balance speed (singles) and accuracy (doubles)
3
Our Goal
Automated analysis techniques to
inform developers about floating
point behavior and make
recommendations regarding the use
of floating point arithmetic.
4
Framework
CRAFT: Configurable Runtime Analysis for
Floating-point Tuning
• Static binary instrumentation
o Read configuration settings
o Replace floating-point instructions with new code
o Rewrite modified binary
• Dynamic analysis
o Run modified program on representative data set
o Produce results and recommendations
5
Previous Work
• Cancellation detection
o Reports loss of precision due to subtraction
o Paper appeared in WHIST‘11
• Range tracking
o Reports min/max values
• Replacement
o Implements mixed-precision configurations
o Paper to appear in ICS’13
6
Mixed Precision
• Use double precision where necessary
• Use single precision everywhere else
• Can be difficult to implement
1: LU ← PA
2: solve Ly = Pb
3: solve Ux0 = y
4: for k = 1, 2, ... do
5:
rk ← b – Axk-1
6:
solve Ly = Prk
7:
solve Uzk = y
8:
xk ← xk-1 + zk
9:
check for convergence
10: end for
Mixed-precision
linear solver
algorithm
Red text indicates
steps performed in
double-precision
(all other steps are
single-precision)
7
Configuration
8
Implementation
• In-place replacement
o Narrowed focus: doubles  singles
o In-place downcast conversion
o Flag in the high bits to indicate replacement
64
32
16
8
4
0
Double
downcast conversion
Replaced
Double
64
7
F
F
4
D
E
A
Non-signalling NaN
32
16
8
4
0
32
16
8
4
0
D
Single
9
Example
gvec[i,j] = gvec[i,j] * lvec[3] + gvar
1
movsd 0x601e38(%rax, %rbx, 8)  %xmm0
2
mulsd -0x78(%rsp)  %xmm0
3
addsd -0x4f02(%rip)  %xmm0
4
movsd %xmm0  0x601e38(%rax, %rbx, 8)
10
Example
gvec[i,j] = gvec[i,j] * lvec[3] + gvar
3
movsd 0x601e38(%rax, %rbx, 8)  %xmm0
check/replace -0x78(%rsp) and %xmm0
mulss -0x78(%rsp)  %xmm0
check/replace -0x4f02(%rip) and %xmm0
addss -0x20dd43(%rip)  %xmm0
4
movsd %xmm0  0x601e38(%rax, %rbx, 8)
1
2
11
Block Editing (PatchAPI)
original instruction in block
block splits
double  single conversion
initialization
cleanup
check/replace
12
Automated Search
• Manual mixed-precision analysis
o Hard to use without intuition regarding potential replacements
• Automatic mixed-precision analysis
o Try lots of configurations (empirical auto-tuning)
o Test with user-defined verification routine and data set
o Exploit program control structure: replace larger structures
(modules, functions) first
o If coarse-grained replacements fail, try finer-grained
subcomponent replacements
13
System Overview
14
NAS Results
Benchmark
(name.CLASS)
Candidates
Configurations
Tested
Instructions Replaced
% Static
% Dynamic
bt.W
6,647
3,854
76.2
85.7
bt.A
6,682
3,832
75.9
81.6
cg.W
940
270
93.7
6.4
cg.A
934
229
94.7
5.3
ep.W
397
112
93.7
30.7
ep.A
397
113
93.1
23.9
ft.W
422
72
84.4
0.3
ft.A
422
73
93.6
0.2
lu.W
5,957
3,769
73.7
65.5
lu.A
5,929
2,814
80.4
69.4
mg.W
1,351
458
84.4
28.0
mg.A
1,351
456
84.1
24.4
sp.W
4,772
5,729
36.9
45.8
sp.A
4,821
5,044
51.9
43.0
15
AMGmk Results
• Algebraic MultiGrid microkernel
• Multigrid method is highly adaptive
• Good candidate for replacement
• Automatic search
• Complete conversion (100% replacement)
• Manually-rewritten version
• Speedup: 175 sec to 95 sec (1.8X)
• Conventional x86_64 hardware
16
SuperLU Results
• Package for LU decomposition and linear solves
•
Reports final error residual
•
Both single- and double-precision versions
• Verified manual conversion via automatic search
•
Used error from provided single-precision version as threshold
•
Final config matched single-precision profile (99.9% replacement)
Threshold
Instructions Replaced
% Static
Final Error
% Dynamic
1.0e-03
99.1
99.9
1.59e-04
1.0e-04
94.1
87.3
4.42e-05
7.5e-05
91.3
52.5
4.40e-05
5.0e-05
87.9
45.2
3.00e-05
2.5e-05
80.3
26.6
1.69e-05
1.0e-05
75.4
1.6
7.15e-07
1.0e-06
72.6
1.6
4.7e7-07
17
Retrospective
• Twofold original motivation
o Faster computation (raw FLOPs)
o Decreased storage footprint and memory bandwidth
o Domains vary in sensitivity to these parameters
• Computation-centric analysis
o Less insight for memory-constrained domains
o Sometimes difficult to translate instruction-level
recommendations to source code-level transformations
• Data-centric analysis
o Focus on data motion, which is closer to source code-level
structures
18
Current Project
• Memory-based replacement
o Perform all computation in double precision
o Save storage space by storing single-precision values in some
cases
• Implementation
o Register-based computation remains double-precision
o Replace movement instructions (movsd)
o Memory to register: check and upcast
o Register to memory: downcast if configured
o Searching for replaceable writes instead of computes
19
Preliminary Results
Benchmark
(name.CLASS)
Candidates
Writes Replaced
% Static
% Dynamic
cg.W
284
95.4
77.5
ep.W
226
96.0
28.4
ft.W
452
94.2
45.0
lu.W
1,782
68.3
81.3
558
96.2
86.4
1,607
80.7
84.7
mg.W
sp.W
All benchmarks were single core versions compiled by the Intel Fortran
compiler with optimization enabled. Tests were performed on an Intel
workstation with 48GB of RAM running 64-bit Linux.
20
21
22
23
24
25
26
27
Future Work
• Case studies
• Search convergence study
28
Conclusion
Automated binary instrumentation techniques
can be used to implement mixed-precision
configurations for floating point code, and
memory-based replacement provides
actionable results.
29
Thank you!
sf.net/p/crafthpc
30
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