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Intel® SPMD Program Compiler Performance Guide
==============================================
* `Using ISPC Effectively`_
+ `Gather and Scatter`_
+ `8 and 16-bit Integer Types`_
+ `Low-level Vector Tricks`_
+ `The "Fast math" Option`_
+ `"Inline" Aggressively`_
+ `Small Performance Tricks`_
+ `Instrumenting Your ISPC Programs`_
+ `Choosing A Target Vector Width`_
+ `Implementing Reductions Efficiently`_
* `Disclaimer and Legal Information`_
* `Optimization Notice`_
don't use the system math library unless it's absolutely necessary
opt=32-bit-addressing
Using ISPC Effectively
======================
Gather and Scatter
------------------
The CPU is a poor fit for SPMD execution in some ways, the worst of which
is handling of general memory reads and writes from SPMD program instances.
For example, in a "simple" array index:
::
int i = ....;
uniform float x[10] = { ... };
float f = x[i];
Since the index ``i`` is a varying value, the various SPMD program
instances will in general be reading different locations in the array
``x``. Because the CPU doesn't have a gather instruction, the ``ispc``
compiler has to serialize these memory reads, performing a separate memory
load for each running program instance, packing the result into ``f``.
(And the analogous case would happen for a write into ``x[i]``.)
In many cases, gathers like these are unavoidable; the running program
instances just need to access incoherent memory locations. However, if the
array index ``i`` could actually be declared and used as a ``uniform``
variable, the resulting array index is substantially more
efficient. This is another case where using ``uniform`` whenever applicable
is of benefit.
In some cases, the ``ispc`` compiler is able to deduce that the memory
locations accessed are either all the same or are uniform. For example,
given:
::
uniform int x = ...;
int y = x;
return array[y];
The compiler is able to determine that all of the program instances are
loading from the same location, even though ``y`` is not a ``uniform``
variable. In this case, the compiler will transform this load to a regular vector
load, rather than a general gather.
Sometimes the running program instances will access a
linear sequence of memory locations; this happens most frequently when
array indexing is done based on the built-in ``programIndex`` variable. In
many of these cases, the compiler is also able to detect this case and then
do a vector load. For example, given:
::
uniform int x = ...;
return array[2*x + programIndex];
A regular vector load is done from array, starting at offset ``2*x``.
8 and 16-bit Integer Types
--------------------------
The code generated for 8 and 16-bit integer types is generally not as
efficient as the code generated for 32-bit integer types. It is generally
worthwhile to use 32-bit integer types for intermediate computations, even
if the final result will be stored in a smaller integer type.
Low-level Vector Tricks
-----------------------
Many low-level Intel® SSE coding constructs can be implemented in ``ispc``
code. For example, the following code efficiently reverses the sign of the
given values.
::
float flipsign(float a) {
unsigned int i = intbits(a);
i ^= 0x80000000;
return floatbits(i);
}
This code compiles down to a single XOR instruction.
The "Fast math" Option
----------------------
``ispc`` has a ``--fast-math`` command-line flag that enables a number of
optimizations that may be undesirable in code where numerical preceision is
critically important. For many graphics applications, the
approximations may be acceptable. The following two optimizations are
performed when ``--fast-math`` is used. By default, the ``--fast-math``
flag is off.
* Expressions like ``x / y``, where ``y`` is a compile-time constant, are
transformed to ``x * (1./y)``, where the inverse value of ``y`` is
precomputed at compile time.
* Expressions like ``x / y``, where ``y`` is not a compile-time constant,
are transformed to ``x * rcp(y)``, where ``rcp()`` maps to the
approximate reciprocal instruction from the standard library.
"Inline" Aggressively
---------------------
Inlining functions aggressively is generally beneficial for performance
with ``ispc``. Definitely use the ``inline`` qualifier for any short
functions (a few lines long), and experiment with it for longer functions.
Small Performance Tricks
------------------------
Performance is slightly improved by declaring variables at the same block
scope where they are first used. For example, in code like the
following, if the lifetime of ``foo`` is only within the scope of the
``if`` clause, write the code like this:
::
float func() {
....
if (x < y) {
float foo;
... use foo ...
}
}
Try not to write code as:
::
float func() {
float foo;
....
if (x < y) {
... use foo ...
}
}
Doing so can reduce the amount of masked store instructions that the
compiler needs to generate.
Instrumenting Your ISPC Programs
--------------------------------
``ispc`` has an optional instrumentation feature that can help you
understand performance issues. If a program is compiled using the
``--instrument`` flag, the compiler emits calls to a function with the
following signature at various points in the program (for
example, at interesting points in the control flow, when scatters or
gathers happen.)
::
extern "C" {
void ISPCInstrument(const char *fn, const char *note,
int line, int mask);
}
This function is passed the file name of the ``ispc`` file running, a short
note indicating what is happening, the line number in the source file, and
the current mask of active SPMD program lanes. You must provide an
implementation of this function and link it in with your application.
For example, when the ``ispc`` program runs, this function might be called
as follows:
::
ISPCInstrument("foo.ispc", "function entry", 55, 0xf);
This call indicates that at the currently executing program has just
entered the function defined at line 55 of the file ``foo.ispc``, with a
mask of all lanes currently executing (assuming a four-wide Intel® SSE
target machine).
For a fuller example of the utility of this functionality, see
``examples/aobench_instrumented`` in the ``ispc`` distribution. Ths
example includes an implementation of the ``ISPCInstrument`` function that
collects aggregate data about the program's execution behavior.
When running this example, you will want to direct to the ``ao`` executable
to generate a low resolution image, because the instrumentation adds
substantial execution overhead. For example:
::
% ./ao 1 32 32
After the ``ao`` program exits, a summary report along the following lines
will be printed. In the first few lines, you can see how many times a few
functions were called, and the average percentage of SIMD lanes that were
active upon function entry.
::
ao.ispc(0067) - function entry: 342424 calls (0 / 0.00% all off!), 95.86% active lanes
ao.ispc(0067) - return: uniform control flow: 342424 calls (0 / 0.00% all off!), 95.86% active lanes
ao.ispc(0071) - function entry: 1122 calls (0 / 0.00% all off!), 97.33% active lanes
ao.ispc(0075) - return: uniform control flow: 1122 calls (0 / 0.00% all off!), 97.33% active lanes
ao.ispc(0079) - function entry: 10072 calls (0 / 0.00% all off!), 45.09% active lanes
ao.ispc(0088) - function entry: 36928 calls (0 / 0.00% all off!), 97.40% active lanes
...
Choosing A Target Vector Width
------------------------------
By default, ``ispc`` compiles to the natural vector width of the target
instruction set. For example, for SSE2 and SSE4, it compiles four-wide,
and for AVX, it complies 8-wide. For some programs, higher performance may
be seen if the program is compiled to a doubled vector width--8-wide for
SSE and 16-wide for AVX.
For workloads that don't require many of registers, this method can lead to
significantly more efficient execution thanks to greater instruction level
parallelism and amortization of various overhead over more program
instances. For other workloads, it may lead to a slowdown due to higher
register pressure; trying both approaches for key kernels may be
worthwhile.
This option is only available for each of the SSE2, SSE4 and AVX targets.
It is selected with the ``--target=sse2-x2``, ``--target=sse4-x2`` and
``--target=avx-x2`` options, respectively.
Implementing Reductions Efficiently
-----------------------------------
It's often necessary to compute a "reduction" over a data set--for example,
one might want to add all of the values in an array, compute their minimum,
etc. ``ispc`` provides a few capabilities that make it easy to efficiently
compute reductions like these. However, it's important to use these
capabilities appropriately for best results.
As an example, consider the task of computing the sum of all of the values
in an array. In C code, we might have:
::
/* C implementation of a sum reduction */
float sum(const float array[], int count) {
float sum = 0;
for (int i = 0; i < count; ++i)
sum += array[i];
return sum;
}
Of course, exactly this computation could also be expressed in ``ispc``,
though without any benefit from vectorization:
::
/* inefficient ispc implementation of a sum reduction */
uniform float sum(const uniform float array[], uniform int count) {
uniform float sum = 0;
for (uniform int i = 0; i < count; ++i)
sum += array[i];
return sum;
}
As a first try, one might try using the ``reduce_add()`` function from the
``ispc`` standard library; it takes a ``varying`` value and returns the sum
of that value across all of the active program instances.
::
/* inefficient ispc implementation of a sum reduction */
uniform float sum(const uniform float array[], uniform int count) {
uniform float sum = 0;
// Assumes programCount evenly divides count
for (uniform int i = 0; i < count; i += programCount)
sum += reduce_add(array[i+programIndex]);
return sum;
}
This implementation loads a set of ``programCount`` values from the array,
one for each of the program instances, and then uses ``reduce_add`` to
reduce across the program instances and then update the sum. Unfortunately
this approach loses most benefit from vectorization, as it does more work
on the cross-program instance ``reduce_add()`` call than it saves from the
vector load of values.
The most efficient approach is to do the reduction in two phases: rather
than using a ``uniform`` variable to store the sum, we maintain a varying
value, such that each program instance is effectively computing a local
partial sum on the subset of array values that it has loaded from the
array. When the loop over array elements concludes, a single call to
``reduce_add()`` computes the final reduction across each of the program
instances' elements of ``sum``. This approach effectively compiles to a
single vector load and a single vector add for each ``programCount`` worth
of values--very efficient code in the end.
::
/* good ispc implementation of a sum reduction */
uniform float sum(const uniform float array[], uniform int count) {
float sum = 0;
// Assumes programCount evenly divides count
for (uniform int i = 0; i < count; i += programCount)
sum += array[i+programIndex];
return reduce_add(sum);
}
Disclaimer and Legal Information
================================
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Optimization Notice
===================
Intel compilers, associated libraries and associated development tools may
include or utilize options that optimize for instruction sets that are
available in both Intel and non-Intel microprocessors (for example SIMD
instruction sets), but do not optimize equally for non-Intel
microprocessors. In addition, certain compiler options for Intel
compilers, including some that are not specific to Intel
micro-architecture, are reserved for Intel microprocessors. For a detailed
description of Intel compiler options, including the instruction sets and
specific microprocessors they implicate, please refer to the "Intel
Compiler User and Reference Guides" under "Compiler Options." Many library
routines that are part of Intel compiler products are more highly optimized
for Intel microprocessors than for other microprocessors. While the
compilers and libraries in Intel compiler products offer optimizations for
both Intel and Intel-compatible microprocessors, depending on the options
you select, your code and other factors, you likely will get extra
performance on Intel microprocessors.
Intel compilers, associated libraries and associated development tools may
or may not optimize to the same degree for non-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, functionality,
or effectiveness of any optimization on microprocessors not manufactured by
Intel. Microprocessor-dependent optimizations in this product are intended
for use with Intel microprocessors.
While Intel believes our compilers and libraries are excellent choices to
assist in obtaining the best performance on Intel and non-Intel
microprocessors, Intel recommends that you evaluate other compilers and
libraries to determine which best meet your requirements. We hope to win
your business by striving to offer the best performance of any compiler or
library; please let us know if you find we do not.