DreamWork Animation DWA

Copyright © 2015, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others.
Anson Chu (presenter)
& Alex Wells,
& Martin Watt (DWA)
August 11 & 13, 2015
DreamWork Animation (DWA):
How We Achieved a 4x Speedup of
Skin Deformation
with SIMD

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DWA Character with Skin Deformation
4

 The Algorithm
 Vectorization
– Basic techniques
– SBB Integration
– Indirect Access
– Data padding
– Manage uniform data
 Results
 Summary
How we got 4x SIMD speedup
5

Skin Deformation Videos
6

 DWA’s largest and most complex
deformation kernel
 C++ Object-Oriented
– Up to 3 levels of indirect access
 Loop body
– Many levels of nested function calls
– Multiple source files across multiple libraries
– 8625 instructions executed per iteration
 Double-precision math
 Parallel, but no SIMD
Skin Deformation Algorithm - Overview
7

8
Motion System  Skin Deformation
Hierarchy Joints Control Points Curve,
Offsets,
& Attachment Bindings
Skin MeshAttachments bound
to Skin Vertices

 Compute deformed vertex
positions and offsets of skin
mesh
– Control Points are on a curve
 Curve driven by character’s
motion system
– Each vertex Attachment
 1-4 “Control Point(s)”
– Shared (e.g. 3 out of 4)
 Weights, offsets, scales, …
Skin Deformation Algorithm - Detail
0 1 2 3 2 3 4 51 2 3 4
Control Points (Curve)
Skin Mesh
Attachment
0
1
2
3
4
5
9

 SIMD instructions
– Can process multiple data lanes in a single
operation
 One vector register can hold multiple
data values
– SSE4 : 128-bit
 2 doubles or 4 floats
– AVX : 256-bit
 4 doubles or 8 floats
 Vector operations compute math (+, -, *,
/) on all of data lanes at the same time
SIMD = Single Instruction Multiple Data
vector_register_10 1 2 3
Step 4vector_register_10 1 2 3
SIMD += operation
vr1[0]+=vr2[0]
vr1[1]+=vr2[1]
vr1[2]+=vr2[2]
vr1[3]+=vr2[3]
10

 No function calls
– Inlined all calls made from loop body (i.e. “inline” keyword)
– Removed non-inlineable calls (e.g. print statements)
– “#pragma inline recursive” scoping entire loop body
 Single entry, single exit
– Removed data-dependent exit conditions (e.g. early outs)
– Removed exception throwing
Intel® C++ Composer XE (ICC) Vectorization
11
https://software.intel.com/sites/default/files/8c/a9/CompilerAutovectorizationGuide.pdf

Straight-line code
 Removed conditionals
– No “switch” statements
– “if” statements supported (as masked or blended
assignments)
 But best to avoid
 Created templatized inlined functions
– To handle conditionals at compile time
– Branch outside loop instead of at each iteration
ICC Vectorization (Part 2)
12
Code example
on next slide

Templates to avoid conditionals inside loop
void compute() {
switch (mAlgorithm->mVersion)
{
case 0:
computeHelper<0>(); break;
case 1:
computeHelper<1>(); break;
default:
};
}
template<int VersionT>
void computeHelper() {
for (int i = 0; i<mCount; ++i) {
mAlgorithm->computePosition<VersionT>(data[i]);
}
}
template<int VersionT>
inline void computePosition(Data d) {
if (VersionT < 1) {
// support older version
} else {
// run newer version of algorithm
}
}
void compute() {
for (int i=0; i<mCount; ++i) {
mAlgorithm->computePosition(data[i]);
}
}
void computePosition(Data d) {
if (this->mVersion < 1) {
// support older version
} else {
// run newer version of algorithm
}
}
13
Converted “Version”
to template
argument VersionT
Dead code
elimination will
remove one of
these branches
at compile time
“VersionT” (known at
compile time) creates
separate code paths
Hoisted conditional to outside loop
Main Loop
Loop Body

 SIMD Building Blocks (SBB)
 C++ template library
 Abstracts out aspects of creating an efficient data parallel
(SIMD + threading) program
– Via concepts of Containers, Accessors, Kernels, and Engines
– Helps avoid common pitfalls of writing efficient SIMD code
 Compatibility
– Compatible with Intel® Threading Building Blocks (TBB) and
OpenMP*
– Generates correct code with a C++11 compiler
What is SBB?
14
Developed by Alex Wells
(alex.m.wells@intel.com)

 Let compiler (ICC) handle privatization of local variables
in a SIMD loop
– Each SIMD lane gets a private instance of the variable
 “SBB Primitives” restrict objects to help the compiler
succeed in privatization
– Plain Old Data
– Inlined object members
– No nested arrays in objects
– No virtual functions
Follow SBB’s Vectorization Strategy
15
Code example
on next slide

Converting to a SBB Primitive (Example)
class Vec3 {
//...
double length();
double v[3];
//...
};
class Vec3 {
//...
inline double length();
double x;
double y;
double z;
//...
};
Individual
data members
16

 ICC optimization report on vectorization
– Compile with flag: “-qopt-report=5 -qopt-report-phase=vec”
– Provides per loop summary of vectorization and estimated
performance impact
– Helps isolate what prevented vectorization of a loop
 Intel® VTune™ Amplifier XE
– Analyzed performance (used Task API)
– Inspected assembly that actually executes
Useful Tools
17

 ICC compiler tells us…
– “Could vectorize but may be inefficient”
 Successfully forced vectorization with #pragma simd
– Actual runtime showed essentially no improvement
 Opt-report
– “Estimates” a slowdown
 Needed investigate into why
“Could vectorize but may be inefficient”
18
Use Opt-Report to investigate obstacles to efficient SIMD code

 Indirect memory addressing
– Gather : indexed reads (“loads”)
– Scatter : indexed writes (“stores”)
 Gathers or scatters can degrade
SIMD performance
 We want to avoid indirect accesses
 So, is there any loop-invariant data
that we can treat as uniform across
loop?
Opt-report littered w/ “indirect access” remarks
// Gather
for (i = 0; i<N; ++i)
x[i] = y[idx[i]];
// Scatter
for (i = 0; i<N; ++i)
y[idx[i]] = x[i];
19
Loop index is used to look
up another index

 Most data is accessed via
ControlPoint index
– Up to 3 levels of indirection
– Causing chains of gathers
 Can we refactor algorithm such
that ControlPoint indices
would be loop invariant?
Algorithm Has Too Many Indirections
AttachmentData array
ControlPoint array
XformData array
Matrix3, Vec3,
double, int, …XformData
i
j
k
20
Example on
next slide

Sort, Hoist Uniform Data, & Sub-Loop
21
Original Attachments
No uniformity
0 1 2 3
2 3 4 5
2 3 4 5
2 3 4 5
1 2 3 4
0 1 2 3
1 2 3 4
0 1 2 3
0 1 2 3
0
1
2
8
Trip count = 9
Sorted: Each sub-loop has uniform ControlPoints.
Sub-loop #3
Uniform: (2,3,4,5)
2 3 4 5
2 3 4 5
8
6
0 1 2 3
0 1 2 3
0 1 2 3
0 1 2 3
0
1
2
Sub-loop #1
Uniform: (0,1,2,3)
3
1 2 3 4
1 2 3 4 Sub-loop #2
Uniform: (1,2,3,4)
5
4
Control Point Indexes
2 3 4 57
6
3
5
4
7

Introduced data sorting…
– To break up Attachments into many sub-loops,
 Each with uniform set of ControlPoints
– Vectorize over each sub-loop
 Eliminating many indirect accesses
Attachment data is non-changing
– So only have to do sort once
Algorithm with uniform ControlPoints
Pull loop-invariant data outside loop
22
Code example
on next slide

Hoisting ControlPoints outside loop (Example)
// Vectorized loop
for (int i = 0; i < count; i++)
{
AttachmentData& a = mAttachments[i];
ControlPoint& cp0 = mControlPoints[a.index0];
computePosition(a, cp0, cp1, cp2, cp3);
}
for (int i = ; i < mSubLoops.count; i++)
{
SubLoop& ss = mSubLoops[i];
ControlPoint& cp0 = mControlPoints[ss.index0];
// Vectorized sub-loop over Attachments with
// loop-invariant ControlPoints
for (int j = ss.begin; j < ss.end; j++) {
AttachmentData& a = mAttachments[j];
computePosition(a, cp0, cp1, cp2, cp3);
}
}
Loop over unsorted Attachments
1. Added sub-loop
2. Hoisted ControlPoints outside vectorized sub-loop
23

 Resolved all “indirect access”
remarks
– No more gather instructions
Impact of Uniform ControlPoints
Mine input data for Uniformity
XformData array
XformData
ControlPoint
Matrix3, Vec3,
double, int, …
Set of (1-4) ControlPoint’s
are uniform over loop
24
2x Speedup (AVX)

 Main SIMD loop always starts and ends on SIMD boundaries
– Processes N SIMD Lane Count (e.g. 4 doubles) iterations at a time
 Peel loop - any iterations up to before first SIMD boundary
 Remainder loop - any iterations from after the last SIMD boundary
to the end of the iteration space
Anatomy of a SIMD Loop
25
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
Loop from 3 through 18
Peel Loop
(scalar)
Remainder Loop
(scalar or masked SIMD)
Main SIMD Loop
Example of
4 Vector Lanes

VTune Shows Execution Time
Screenshot from Intel® VTune Amplifier XE
26
Main Loop
(vectorized)
Remainder Loop
Peel Loop
Blue marks
alongside scrollbar
indicate where
execution time is
spent in assembly

Investigated trip counts of the sub-loops
And observed short trip counts
– We had 4 to 32 or more iterations
– Enough to cover vectorization
Start and end of iteration space of sub-loops
did not align with SIMD lane boundaries
– Spending over 35% in Peel and Remainder loops
Short Trip Counts
27

 Added padded entries into our input data stream
 Ensured iterations started and ended on a SIMD lane
boundary
– 2 (SSE4.2), 4 (AVX)
– Attachment data is non-changing, so only have to do
padding once
 For padded entries, just repeat work
– Scatter result to same output address
Implemented data padding
28
Example on
next slide

Padded Attachments
0 1 2 3
0 1 2 3
0 1 2 3
0 1 2 3
1 2 3 4
1 2 3 4
0
1
2
1 2 3 4
0 1 2 3
0 1 2 3
8
Not padded
Sub-loop #1
Trip count = 6
Sub-loop #2
Trip count = 3
0 1 2 3
0 1 2 3
0 1 2 3
0 1 2 3
1 2 3 4
1 2 3 4
0
1
2
1 2 3 4
1 2 3 4
0 1 2 3
0 1 2 3
0 1 2 3
0 1 2 3
3
4
5
6
7
8
9
10
11
Padded to 4 data lanes
Sub-loop #1
Trip count = 8
Sub-loop #2
Trip count = 4
Padded
Entries
Control Point
Indexes
29
3
4
5
6
7
Main SIMD
Loop
Remainder
Loop
Peel
Loop
Example of
4 Vector Lanes

Execution Time : Not Padded vs. Padded
Screenshots from
Intel® VTune Amplifier XE
30
PaddedNot Padded
All time spent
in Main SIMD
Loop
Blue marks alongside
scrollbar indicates
where executed time
is spent in assembly

 Allowed all of the time to be spent in our aligned
vectorized code
 Speedup overcame the processing overhead of padded
data
Data Padding Impact
Pad data to align with SIMD lane boundaries
31
14% more
speedup

 After padding, compiler (icc14) chose to unroll our
vectorized AVX loop
– Caused it to iterate by 8 element rather than 4
 Since our we are padding to boundary of 4
– Once again, spending time in the peel and remainder loops
 Resolved by explicitly setting SIMD length
– #pragma simd vectorlength(4)
Encountered obstacle to padding
Avoid surprises. Explicitly set vector length.
32

 Inspecting assembly (VTune), we noticed room for
improvement
 Data is uniform for entire loop,
– But is still accessed via indirection at each loop iteration
 So we refactored algorithm to manually manage all
indirect accesses (from ControlPoint indexes) outside of
loop
– Pre-populate all indirect data into uniform data structure
Uniform Indirection Overhead
33

 Before entering a SIMD loop
– For each uniform value the compiler may
 Load scalar value into register
 Broadcast scalar value in register to all lanes of SIMD register
 Store SIMD register to new location on stack for use inside the
SIMD loop body
– Multiply overhead by up to 59 doubles per Control Point
 For long trip counts, overhead easily amortized
– But short trip counts can not
Overhead of Uniform Data in SIMD Loops
34

 sbb::UniformSoaOver1d
– Store loop invariant data in a SIMD ready format
 SIMD loops can directly access the data without conversion
– Decouples SIMD conversion overhead from the loop
 Puts user in control of when to incur the overhead
– Over multiple sub-loops
 Enables partial updates of Uniform Data
 Enables reuse of Uniform Data
Uniform Data & SBB
35
Example on
next slide

Partial Updating of Uniform data
0 1 2 3
0 1 2 3
0 1 2 3
0 1 2 3
1 2 3 4
1 2 3 4
0
1
2
1 2 3 4
1 2 3 4
3
4
5
6
7
Loop #1 : Load uniform data for ControlPoints (0,1,2,3)
Loop #2 : Reuse uniform data for (1,2,3). Load (4).
2 3 4 5
2 3 4 5
2 3 4 5
2 3 4 5
8
9
10
11
Loop #3 : Reuse uniform data for (2,3,4). Load (5).
Attachment
Indexes
Control Points
Sub-Loop #
36

 From one sub-loop to the next, on average, we only
update ¼ of the UniformData
– Save 75% of overhead involved in setting up uniform data
Impact of Managing Uniform Data
Explicitly manage uniform data
37
13% more
speedup

 Performance data collected using VTune
– CPU Time
– Instructions Executed
 Hardware: SandyBridge, Xeon (dual-socket, 16-core)
 Single-threaded execution
 Comparison definitions
– Baseline
 Original algorithm
– New algorithm
 Scalar - Vectorization disabled
 SIMD - Vectorization enabled
Results
38

Results: Same Work, Less Instructions
39
8.5x improvement
(AVX)
Lower is better

Results: Algorithm Speedups
Higher is better
4.2x total speedup
(AVX)
40
1.5x Scalar
speedup

Results: Do More in Less Time
41
SIMD,
Double Resolution
Lower is better
Original algorithm,
1x Vertices

Biggest payoff was from eliminating all
indirection
Padded data to align with SIMD lane boundaries
Explicitly managed and re-used uniform data
Summary
42
4.2x total
speedup!

 Anson Chu : anson.chu@intel.com
 Alex Wells : alex.m.wells@intel.com
 For SBB inquiries, contact Alex!
Thank you!
43

Optimization Notice
44
Optimization Notice
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instruction sets that are available in both Intel® and non-Intel microprocessors (for example SIMD instruction sets), but do not
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Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include Intel®
Streaming SIMD Extensions 2 (Intel® SSE2), Intel® Streaming SIMD Extensions 3 (Intel® SSE3), and Supplemental Streaming
SIMD Extensions 3 (Intel® SSSE3) instruction sets and other optimizations. Intel does not guarantee the availability,
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While Intel believes our compilers and libraries are excellent choices to assist in obtaining the best performance on Intel® and
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us know if you find we do not.
Notice revision #20101101

C o p y r i g h t © 2 0 1 5 , I n t e l C o r p o r a t i o n . A l l r i g h t s r e s e r v e d . *O t h e r n a me s a n d b r a n d s ma y b e c l a i me d a s t h e p r o p e r t y o f o t h e r s .

Backup Slides

 Manually define copy constructor for objects
– Compiler had trouble with some objects
– Even though logically equivalent to compiler provided
default
 Allows privatization of local variable instances inside vector loop
 Replaced ‘bool’ with ‘int’
– ‘bool’ is not supported for SIMD privatization
Opt-report “unsupported data type” remark
May need to manually define copy constructor
47

 __builtin_expect
– Added to conditionals when control flow follows a known path
– Allows compiler to test all data lanes to see that the expression
was zero and skip a code block
 Faster than following a masked blended code path
– Used carefully, and verified doesn’t hurt performance
Other incremental improvements
48

 Okay to divide by zero
– Result will be NAN
 Any subsequent operations with NAN, results in NAN
– Removed multiple conditional tests for zero when dividing
 Continue computation
 Test final result for NAN (if necessary)
– Hint: combine with ‘__builtin_expect’
Other incremental improvements (Part 2)
49
#define IS_NAN(val)
((val == val) == false)

 If you store bool values as anything other than 32-bit,
compiler needs to do extra work to test for true/false
– In C++, a Boolean is:
 False, when 0x0
 True, when anything else
– But in SSE/AVX SIMD,
 True when 'high bit set‘
 False otherwise
 So we used int instead of bool
– Tested explicitly against integer (0 or 1)
Other incremental improvements (Part 3)
50

 Let SBB Containers & Accessors…
– Automatically handle data transformation and alignment
 Let SBB Engines…
– Handle loop iteration
 Let SBB Primitive and underlying compiler (ICC)…
– Handle privatization of local variables in a SIMD loop
 Such that a private instance exists for each SIMD lane
SBB Integration
51

 Converted data containers
– sbb::Soa1dContainer (instead of std::vector)
– Use SBB Accessors to read/write data from/to Containers
 Converted algorithm loop
– SBB Kernels
 Define unit of work (i.e. loop body)
– SBB Engines
 Control code generation (e.g Scalar vs. Simd)
– Control peel or remainder loop generation
– Set number of SIMD lanes
 Control threading model (Single-threaded, TBB, OpenMP) and grain
size.
SBB Integration – Containers, Kernels, Engines
52

 Converted objects in DWA’s 3D math library
– Meet SBB Primitive requirements
– Example:
SBB Integration
// Math library’s 3D Vector
class Vec3 {
//...
protected:
double v[3];
//...
};
#include <SBB/Primitive.h>
class Vec3 {
//...
public:
double x;
double y;
double z;
//...
};
SBB_PRIMITIVE(Vec, x, y, z)
53

SBB Primitive Nesting(Example)
#include <SBB/Primitive.h>
class Vec3 {
public:
double x;
double y;
double z;
};
SBB_PRIMITIVE(Vec3, x, y, z)
struct AttachmentData {
double weight;
math::Vec3 offset;
double scale;
…
};
SBB_PRIMITIVE(AttachmentData,
weight,
offset,
scale,
…);
Primitives can
be nested
54

 Provides familiarity of AOS Object-Oriented
programming,
– But stores data in SIMD efficient format internally (e.g. SOA)
– API similar to std::vector<Struct>
 Used sbb::Soa1dContainer
SBB Integration: Container
class AttachmentPartition
{
// Array of structures (AOS)
std::vector<Attachment> mData;
}
class AttachmentPartition
{
// Easily switch between types of memory layout
//typedef sbb::Aos1dContainer<Attachment> SbbContainer;
typedef sbb::Soa1dContainer<Attachment> SbbContainer;
SbbContainer mData;
}
55

 Converted loop
to SBB kernel
 Use SBB
Accessors
– To read/write
data from/to
Containers,
from within
loop
SBB Integration: Kernel and Accessors
// SBB accessors to input and output arrays
auto sbbInputs = mPartition.mData->access();
sbb::Aos1dAccessor<AffectedPosition, sbb::AccessByStride>
sbbOutputs(&(mAffectedPositions.rawDataArray()[0]), mCount);
// Define SBB Kernel (loop body)
SBB_KERNEL_BEGIN(kernel)
SBB_ITER_D1_BEGIN(index)
{
// Get SBB accessor to individual element
auto attachment = sbbInputs[index];
gmath::Vec3d affectedPos;
affectedPos = mAlgorithm->computePosition(attachment);
affectedPos *= attachment.weight();
sbbOutputs[index] = affectedPos;
}
SBB_ITER_END
SBB_KERNEL_END
auto is your SBB
friend 
56

 Used SBB Engine to…
 Control code generation (e.g Scalar vs. Simd)
– Control peel or remainder loop generation
– Set number of SIMD lanes
 Control threading model (and grain size)
– TBB, OpenMP, or single-threaded
 [Example on next slide…]
SBB Integration: Engine
57

SBB Integration : Engine (example)
SBB_KERNEL_BEGIN(loop)
…
SBB_KERNEL_END
// SBB Engine
sbb::Block1dBounds bounds(0, count);
sbb::Block1dSize blockSize(16);
Engine engine;
engine.run(loop, bounds, blockSize);
// Easily switch between Code Strategy (i.e. SIMD vs. non-SIMD)
typedef sbb::SimdCode<SimdLaneCount, sbb::BoundsAreSimdAlignedYes> CodeGen;
//typedef sbb::VectorCode<SimdLaneCount, sbb::BoundsAreSimdAlignedYes> CodeGen;
//typedef sbb::ScalarCode<SimdLaneCount, sbb::BoundsAreSimdAlignedYes> CodeGen;
// Easily switch between SBB Engines
typedef sbb::Tbb1dEngine<CodeGen> Engine;
//typedef sbb::SingleThreaded1dEngine<CodeGen> Engine;
typedef’s are
useful for
debugging
and
performance
tuning
58

 Array of Structures (AOS)
– Ex. std::vector<gmath::Vec3d> A
– Multiple (load, shuffles, or gather)
instructions to get data into a vector
register
– Possibly access multiple cache lines
 Structures of Arrays (SOA)
– Ex. struct A {double x[N], y[N], z[N]}
– All ‘x’ is adjacent in memory
– Often, single vector load instruction
AOS vs. SOA
X
A[i+0]
Y Z X
A[i+1]
Y Z X
A[i+2]
Y Z X
A[i+3]
Y Z
AOS in Memory Layout
A
Z[i+0] Z[i+1] Z[i+2] Z[i+3]
Y[i+0] Y[i+1] Y[i+2] Y[i+3]
X[i+0] X[i+1] X[i+2] X[i+3]
SOA in Memory Layout
59

 Definitely, improves assembly inside a vectorized loop
 But, there’s a cost to adding a separate AOS to SOA
conversion
– Would likely eat up any improvements
– Could be worth it,
 If data is accessed multiple times or by a multi-pass algorithm
 Ideally, avoid doing any conversion
– Keep the data as SOA the entire time
Is converting to SOA worthwhile?
60

 Intel® C++ Composer XE (ICC) can generate SIMD code
for us implicitly or explicitly:
– Implicit
 Compiler decides its safe to do so and just does it
 aka “auto-vectorization”
– Explicit
 Programmer declares its safe and/or worthwhile
 Ex. “#pragma simd”
Intel® C++ Composer XE (ICC) : Vectorization
61

 Find and replace ‘size_t’ with ‘int’
– Help avoid 64-bit indexes
– Got rid of “64bit indexed” remarks for a single level of
indirection
Resolved opt-report “64bit indexed” remarks
62

Results: Vectorization Efficiency
AVX (scalar & vector) gains
efficiency due to 3
operand instructions, so
more work represented in
fewer instructions
Higher is better
63

Results: SIMD Speedups
Higher is better
64

 ICC opt-report
– Diagnostic messages do not always report exact culprit or
line number
– Errors and remarks are sometimes vague and ambiguous
– But, ICC continues to improve this in newer versions
 Inlined code from several different files
– So it was guesswork, looking at line number of each file for
suspect code
Difficulties
65

 Inlining code can change execution order of math operations
– Still correct, but results may differ
 Due to floating point rounding differences
 Bloated compile time
– Enormous size of inlined loop body (likely uncommon)
– Workaround was to separate our several variations of loop into
multiple compilation units, to allow parallel compilation
 Intel® Advisor XE 2016 with Vectorization Advisor
– Was not available at the time
– May have been useful tool for vectorization of this algorithm
Difficulties (Part 2)
66

 Try various instruction sets (SSE4.2, AVX, AVX2).
– May get additional diagnostics
– e.g. AVX2 reported “indirect access” and “64bit indexed”
remarks
 Try both ‘#pragma simd’ and ‘#pragma vector’ with opt-
report’s
– ‘#pragma ivdep’ with ‘#pragma vector’ proved more useful
than ‘#pragma simd’
Things To Try When Vectorizing
67

68
 Try different compiler versions
– ICC 15 improved formatting over ICC 14
– And sometimes more useful information
 If line numbers are vague from opt-report
– Try commenting out lines to isolate culprit line number
– Try commenting out code blocks until it works
 E.g. binary search strategy
Things to try when vectorizing (Part 2)

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