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docs/perfguide.rst
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Arina Neshlyaeva
perfguide: fix iteration space for foreach
11 авг 2024, 06:24
11 авг 2024, 06:24
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============================= Intel® ISPC Performance Guide ============================= The SPMD programming model provided by ``ispc`` naturally delivers excellent performance for many workloads thanks to efficient use of CPU SIMD vector hardware. This guide provides more details about how to get the most out of ``ispc`` in practice. * `Key Concepts`_ + `Efficient Iteration With "foreach"`_ + `Improving Control Flow Coherence With "foreach_tiled"`_ + `Using Coherent Control Flow Constructs`_ + `Use "uniform" Whenever Appropriate`_ + `Use "Structure of Arrays" Layout When Possible`_ * `Tips and Techniques`_ + `Understanding Gather and Scatter`_ + `Avoid 64-bit Addressing Calculations When Possible`_ + `Avoid Computation With 8 and 16-bit Integer Types`_ + `Implementing Reductions Efficiently`_ + `Using "foreach_active" Effectively`_ + `Using Low-level Vector Tricks`_ + `The "Fast math" Option`_ + `"inline" Aggressively`_ + `Avoid The System Math Library`_ + `Declare Variables In The Scope Where They're Used`_ + `Instrumenting Intel® ISPC Programs To Understand Runtime Behavior`_ + `Choosing A Target Vector Width`_ * `Notices & Disclaimers`_ Key Concepts ============ This section describes the four most important concepts to understand and keep in mind when writing high-performance ``ispc`` programs. It assumes good familiarity with the topics covered in the ``ispc`` `Users Guide`_. .. _Users Guide: ispc.html Efficient Iteration With "foreach" ---------------------------------- The ``foreach`` parallel iteration construct is semantically equivalent to a regular ``for()`` loop, though it offers meaningful performance benefits. (See the `documentation on "foreach" in the Users Guide`_ for a review of its syntax and semantics.) As an example, consider this simple function that iterates over some number of elements in an array, doing computation on each one: .. _documentation on "foreach" in the Users Guide: ispc.html#parallel-iteration-statements-foreach-and-foreach-tiled :: export void foo(uniform int a[], uniform int count) { for (int i = programIndex; i < count; i += programCount) { // do some computation on a[i] } } Depending on the specifics of the computation being performed, the code generated for this function could likely be improved by modifying the code so that the loop only goes as far through the data as is possible to pack an entire gang of program instances with computation each time through the loop. Doing so enables the ``ispc`` compiler to generate more efficient code for cases where it knows that the execution mask is "all on". Then, an ``if`` statement at the end handles processing the ragged extra bits of data that didn't fully fill a gang. :: export void foo(uniform int a[], uniform int count) { // First, just loop up to the point where all program instances // in the gang will be active at the loop iteration start uniform int countBase = count & ~(programCount-1); for (uniform int i = 0; i < countBase; i += programCount) { int index = i + programIndex; // do some computation on a[index] } // Now handle the ragged extra bits at the end int index = countBase + programIndex; if (index < count) { // do some computation on a[index] } } While the performance of the above code will likely be better than the first version of the function, the loop body code has been duplicated (or has been forced to move into a separate utility function). Using the ``foreach`` looping construct as below provides all of the performance benefits of the second version of this function, with the compactness of the first. :: export void foo(uniform int a[], uniform int count) { foreach (i = 0 ... count) { // do some computation on a[i] } } Improving Control Flow Coherence With "foreach_tiled" ----------------------------------------------------- Depending on the computation being performed, ``foreach_tiled`` may give better performance than ``foreach``. (See the `documentation in the Users Guide`_ for the syntax and semantics of ``foreach_tiled``.) Given a multi-dimensional iteration like: .. _documentation in the Users Guide: ispc.html#parallel-iteration-statements-foreach-and-foreach-tiled :: foreach (j = 0 ... height, i = 0 ... width) { // do computation on element (i,j) } if the ``foreach`` statement is used, elements in the gang of program instances will be mapped to values of ``i`` and ``j`` by taking spans of ``programCount`` elements across ``i`` with a single value of ``j``. For example, the ``foreach`` statement above roughly corresponds to: :: for (uniform int j = 0; j < height; ++j) for (int i = 0; i < width; i += programCount) { // do computation } When a multi-dimensional domain is being iterated over, ``foreach_tiled`` statement maps program instances to data in a way that tries to select square n-dimensional segments of the domain. For example, on a compilation target with 8-wide gangs of program instances, it generates code that iterates over the domain the same way as the following code (though more efficiently): :: for (int j = programIndex/4; j < height; j += 2) for (int i = programIndex%4; i < width; i += 4) { // do computation } Thus, each gang of program instances operates on a 2x4 tile of the domain. With higher-dimensional iteration and different gang sizes, a similar mapping is performed--e.g. for 2D iteration with a 16-wide gang size, 4x4 tiles are iterated over; for 4D iteration with a 8-gang, 1x2x2x2 tiles are processed, and so forth. Performance benefit can come from using ``foreach_tiled`` in that it essentially optimizes for the benefit of iterating over *compact* regions of the domain (while ``foreach`` iterates over the domain in a way that generally allows linear memory access.) There are two benefits from processing compact regions of the domain. First, it's often the case that the control flow coherence of the program instances in the gang is improved; if data-dependent control flow decisions are related to the values of the data in the domain being processed, and if the data values have some coherence, iterating with compact regions will improve control flow coherence. Second, processing compact regions may mean that the data accessed by program instances in the gang is more coherent, leading to performance benefits from better cache hit rates. As a concrete example, for the ray tracer example in the ``ispc`` distribution (in the ``examples/rt`` directory), performance is 20% better when the pixels are iterated over using ``foreach_tiled`` than ``foreach``, because more coherent regions of the scene are accessed by the set of rays in the gang of program instances. Using Coherent Control Flow Constructs -------------------------------------- Recall from the ``ispc`` Users Guide, in the `SPMD-on-SIMD Execution Model section`_ that ``if`` statements with a ``uniform`` test compile to more efficient code than ``if`` tests with varying tests. The coherent ``cif`` statement can provide many benefits of ``if`` with a uniform test in the case where the test is actually varying. .. _SPMD-on-SIMD Execution Model section: ispc.html#the-spmd-on-simd-execution-model In this case, the code the compiler generates for the ``if`` test is along the lines of the following pseudo-code: :: bool expr = /* evaluate cif condition */ if (all(expr)) { // run "true" case of if test only } else if (!any(expr)) { // run "false" case of if test only } else { // run both true and false cases, updating mask appropriately } For ``if`` statements where the different running SPMD program instances don't have coherent values for the boolean ``if`` test, using ``cif`` introduces some additional overhead from the ``all`` and ``any`` tests as well as the corresponding branches. For cases where the program instances often do compute the same boolean value, this overhead is worthwhile. If the control flow is in fact usually incoherent, this overhead only costs performance. In a similar fashion, ``ispc`` provides ``cfor``, ``cwhile``, and ``cdo`` statements. These statements are semantically the same as the corresponding non-"c"-prefixed functions. Use "uniform" Whenever Appropriate ---------------------------------- For any variable that will always have the same value across all of the program instances in a gang, declare the variable with the ``uniform`` qualifier. Doing so enables the ``ispc`` compiler to emit better code in many different ways. As a simple example, consider a ``for`` loop that always does the same number of iterations: :: for (int i = 0; i < 10; ++i) // do something ten times If this is written with ``i`` as a ``varying`` variable, as above, there's additional overhead in the code generated for the loop as the compiler emits instructions to handle the possibility of not all program instances following the same control flow path (as might be the case if the loop limit, 10, was itself a ``varying`` value.) If the above loop is instead written with ``i`` ``uniform``, as: :: for (uniform int i = 0; i < 10; ++i) // do something ten times Then better code can be generated (and the loop possibly unrolled). In some cases, the compiler may be able to detect simple cases like these, but it's always best to provide the compiler with as much help as possible to understand the actual form of your computation. Use "Structure of Arrays" Layout When Possible ---------------------------------------------- In general, memory access performance (for both reads and writes) is best when the running program instances access a contiguous region of memory; in this case efficient vector load and store instructions can often be used rather than gathers and scatters. As an example of this issue, consider an array of a simple point datatype laid out and accessed in conventional "array of structures" (AOS) layout: :: struct Point { float x, y, z; }; uniform Point pts[...]; float v = pts[programIndex].x; In the above code, the access to ``pts[programIndex].x`` accesses non-sequential memory locations, due to the ``y`` and ``z`` values between the desired ``x`` values in memory. A "gather" is required to get the value of ``v``, with a corresponding decrease in performance. If ``Point`` was defined as a "structure of arrays" (SOA) type, the access can be much more efficient: :: struct Point8 { float x[8], y[8], z[8]; }; uniform Point8 pts8[...]; int majorIndex = programIndex / 8; int minorIndex = programIndex % 8; float v = pts8[majorIndex].x[minorIndex]; In this case, each ``Point8`` has 8 ``x`` values contiguous in memory before 8 ``y`` values and then 8 ``z`` values. If the gang size is 8 or less, the access for ``v`` will have the same value of ``majorIndex`` for all program instances and will access consecutive elements of the ``x[8]`` array with a vector load. (For larger gang sizes, two 8-wide vector loads would be issues, which is also quite efficient.) However, the syntax in the above code is messy; accessing SOA data in this fashion is much less elegant than the corresponding code for accessing the data with AOS layout. The ``soa`` qualifier in ``ispc`` can be used to cause the corresponding transformation to be made to the ``Point`` type, while preserving the clean syntax for data access that comes with AOS layout: :: soa<8> Point pts[...]; float v = pts[programIndex].x; Thanks to having SOA layout a first-class concept in the language's type system, it's easy to write functions that convert data between the layouts. For example, the ``aos_to_soa`` function below converts ``count`` elements of the given ``Point`` type from AOS to 8-wide SOA layout. (It assumes that the caller has pre-allocated sufficient space in the ``pts_soa`` output array. :: void aos_to_soa(uniform Point pts_aos[], uniform int count, soa<8> pts_soa[]) { foreach (i = 0 ... count) pts_soa[i] = pts_aos[i]; } Analogously, a function could be written to convert back from SOA to AOS if needed. Tips and Techniques =================== This section introduces a number of additional techniques that are worth keeping in mind when writing ``ispc`` programs. Understanding Gather and Scatter -------------------------------- Memory reads and writes from the program instances in a gang that access irregular memory locations (rather than a consecutive set of locations, or a single location) can be relatively inefficient. As an example, consider the "simple" array indexing calculation below: :: int i = ....; uniform float x[10] = { ... }; float f = x[i]; Since the index ``i`` is a varying value, the program instances in the gang will in general be reading different locations in the array ``x``. Because not all CPUs 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``. (The analogous case happens for a write into ``x[i]``.) In many cases, gathers like these are unavoidable; the program instances just need to access incoherent memory locations. However, if the array index ``i`` actually has the same value for all of the program instances or if it represents an access to a consecutive set of array locations, much more efficient load and store instructions can be generated instead of gathers and scatters, respectively. In many cases, the ``ispc`` compiler is able to deduce that the memory locations accessed by a varying index 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: :: for (int i = programIndex; i < count; i += programCount) // process array[i]; Regular vector loads and stores are issued for accesses to ``array[i]``. Both of these cases have been ones where the compiler is able to determine statically that the index has the same value at compile-time. It's often the case that this determination can't be made at compile time, but this is often the case at run time. The ``reduce_equal()`` function from the standard library can be used in this case; it checks to see if the given value is the same across over all of the running program instances, returning true and its ``uniform`` value if so. The following function shows the use of ``reduce_equal()`` to check for an equal index at execution time and then either do a scalar load and broadcast or a general gather. :: uniform float array[..] = { ... }; float value; int i = ...; uniform int ui; if (reduce_equal(i, &ui) == true) value = array[ui]; // scalar load + broadcast else value = array[i]; // gather For a simple case like the one above, the overhead of doing the ``reduce_equal()`` check is likely not worthwhile compared to just always doing a gather. In more complex cases, where a number of accesses are done based on the index, it can be worth doing. See the example ``examples/volume_rendering`` in the ``ispc`` distribution for the use of this technique in an instance where it is beneficial to performance. Understanding Memory Read Coalescing ------------------------------------ XXXX todo Avoid 64-bit Addressing Calculations When Possible -------------------------------------------------- Even when compiling to a 64-bit architecture target, ``ispc`` does many of the addressing calculations in 32-bit precision by default--this behavior can be overridden with the ``--addressing=64`` command-line argument. This option should only be used if it's necessary to be able to address over 4GB of memory in the ``ispc`` code, as it essentially doubles the cost of memory addressing calculations in the generated code. Avoid Computation With 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. 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; } Exactly this computation could also be expressed as a purely uniform computation 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; foreach (i = 0 ... count) sum += reduce_add(array[i]); return sum; } This implementation loads a gang's worth of 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 loop iteration's 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; foreach (i = 0 ... count) sum += array[i]; return reduce_add(sum); } Using "foreach_active" Effectively ---------------------------------- For high-performance code, For example, consider this segment of code, from the introduction of ``foreach_active`` in the ispc User's Guide: :: uniform float array[...] = { ... }; int index = ...; foreach_active (i) { ++array[index]; } Here, ``index`` was assumed to possibly have the same value for multiple program instances, so the updates to ``array[index]`` are serialized by the ``foreach_active`` statement in order to not have undefined results when ``index`` values do collide. The code generated by the compiler can be improved in this case by making it clear that only a single element of the array is accessed by ``array[index]`` and that thus a general gather or scatter isn't required. Specifically, by using the ``extract()`` function from the standard library to extract the current program instance's value of ``index`` into a ``uniform`` variable and then using that to index into ``array``, as below, more efficient code is generated. :: foreach_active (instanceNum) { uniform int unifIndex = extract(index, instanceNum); ++array[unifIndex]; } Using Low-level Vector Tricks ----------------------------- Many low-level Intel® SSE and AVX coding constructs can be implemented in ``ispc`` code. The ``ispc`` standard library functions ``intbits()`` and ``floatbits()`` are often useful in this context. Recall that ``intbits()`` takes a ``float`` value and returns it as an integer where the bits of the integer are the same as the bit representation in memory of the ``float``. (In other words, it does *not* perform an integer to floating-point conversion.) ``floatbits()``, then, performs the inverse computation. As an example of the use of these functions, 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 ``--opt=fast-math`` command-line flag that enables a number of optimizations that may be undesirable in code where numerical precision is critically important. For many graphics applications, for example, the approximations introduced may be acceptable, however. The following two optimizations are performed when ``--opt=fast-math`` is used. By default, the ``--opt=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 ``ispc`` 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. Avoid The System Math Library ----------------------------- The default math library for transcendentals and the like in ``ispc`` has higher error than the system's math library, though is much more efficient due to being vectorized across the program instances and due to the fact that the functions can be inlined in the final code. (It generally has errors in the range of 10ulps, while the system math library generally has no more than 1ulp of error for transcendentals.) If the ``--math-lib=system`` command-line option is used when compiling an ``ispc`` program, then calls to the system math library will be generated instead. This option should only be used if the higher precision is absolutely required as the performance impact of using it can be significant. Declare Variables In The Scope Where They're Used ------------------------------------------------- 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 Intel® ISPC Programs To Understand Runtime Behavior ----------------------------------------------------------------- ``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, uint64_t 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 program instances in the gang. 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, 0xfull); 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 gang size target machine). For a fuller example of the utility of this functionality, see ``examples/aobench_instrumented`` in the ``ispc`` distribution. This 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. Notices & Disclaimers ===================== Performance varies by use, configuration and other factors. Learn more at www.intel.com/PerformanceIndex.