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@ -23,45 +23,181 @@ xmlns:xlink="http://www.w3.org/1999/xlink" xml:id="section_techniques">
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<section>
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<title>Help the Compiler Help You</title>
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<para>
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Start with scalar code, which is the most portable. Use various
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tricks for helping the compiler vectorize scalar code. Make
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sure you align your data on 16-byte boundaries wherever
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possible, and tell the compiler it's aligned. Use __restrict__
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pointers to promise data does not alias.
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The best way to use vector intrinsics is often <emphasis>not to
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use them at all</emphasis>.
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</para>
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<para>
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This may seem counterintuitive at first. Aren't vector
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intrinsics the best way to ensure that the compiler does exactly
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what you want? Well, sometimes. But the problem is that the
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best instruction sequence today may not be the best instruction
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sequence tomorrow. As the PowerISA moves forward, new
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instruction capabilities appear, and the old code you wrote can
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easily become obsolete. Then you start having to create
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different versions of the code for different levels of the
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PowerISA, and it can quickly become difficult to maintain.
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</para>
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<para>
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Most often programmers use vector intrinsics to increase the
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performance of loop kernels that dominate the performance of an
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application or library. However, modern compilers are often
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able to optimize such loops to use vector instructions without
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having to resort to intrinsics, using an optimization called
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autovectorization (or auto-SIMD). Your first focus when writing
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loop kernels should be on making the code amenable to
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autovectorization by the compiler. Start by writing the code
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naturally, using scalar memory accesses and data operations, and
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see whether the compiler autovectorizes your code. If not, here
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are some steps you can try:
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</para>
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<itemizedlist>
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<listitem>
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<para>
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<emphasis role="underline">Check your optimization
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level</emphasis>. Different compilers enable
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autovectorization at different optimization levels. For
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example, at this writing the GCC compiler requires
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<code>-O3</code> to enable autovectorization by default.
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</para>
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</listitem>
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<listitem>
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<para>
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<emphasis role="underline">Consider using
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<code>-ffast-math</code></emphasis>. This option assumes
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that certain fussy aspects of IEEE floating-point can be
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ignored, such as the presence of Not-a-Numbers (NaNs),
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signed zeros, and so forth. <code>-ffast-math</code> may
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also affect precision of results that may not matter to your
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application. Turning on this option can simplify the
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control flow of loops generated for your application by
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removing tests for NaNs and so forth. (Note that
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<code>-Ofast</code> turns on both -O3 and -ffast-math in
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GCC.)
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</para>
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</listitem>
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<listitem>
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<para>
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<emphasis role="underline">Align your data wherever
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possible</emphasis>. For most effective auto-vectorization,
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arrays of data should be aligned on at least a 16-byte
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boundary, and pointers to that data should be identified as
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having the appropriate alignment. For example:
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</para>
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<programlisting> float fdata[4096] __attribute__((aligned(16)));</programlisting>
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<para>
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ensures that the compiler can use an efficient, aligned
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vector load to bring data from <code>fdata</code> into a
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vector register. Autovectorization will appear more
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profitable to the compiler when data is known to be
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aligned.
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</para>
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<para>
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You can also declare pointers to point to aligned data,
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which is particularly useful in function arguments:
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</para>
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<programlisting> void foo (__attribute__((aligned(16))) double * aligned_fptr)</programlisting>
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</listitem>
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<listitem>
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<para>
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<emphasis role="underline">Tell the compiler when data can't
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overlap</emphasis>. In C and C++, use of pointers can cause
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compilers to pessimistically analyze which memory references
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can refer to the same memory. This can prevent important
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optimizations, such as reordering memory references, or
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keeping previously loaded values in memory rather than
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reloading them. Inefficiently optimized scalar loops are
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less likely to be autovectorized. You can annotate your
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pointers with the <code>restrict</code> or
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<code>__restrict__</code> keyword to tell the compiler that
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your pointers don't "alias" with any other memory
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references. (<code>restrict</code> can be used only in C
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when compiling for the C99 standard or later.
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<code>__restrict__</code> is a language extension, available
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in both GCC and Clang, that can be used for both C and C++.)
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</para>
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<para>
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Suppose you have a function that takes two pointer
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arguments, one that points to data your function writes to, and
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one that points to data your function reads from. By
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default, the compiler may believe that the data being read
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and written could overlap. To disabuse the compiler of this
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notion, do the following:
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</para>
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<programlisting> void foo (double *__restrict__ outp, double *__restrict__ inp)</programlisting>
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</listitem>
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</itemizedlist>
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</section>
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<section>
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<title>Use Portable Intrinsics</title>
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<para>
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Individual compilers may provide other intrinsic support. Only
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the intrinsics in this manual are guaranteed to be portable
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across compliant compilers.
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If you can't convince the compiler to autovectorize your code,
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or you want to access specific processor features not
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appropriate for autovectorization, you should use intrinsics.
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However, you should go out of your way to use intrinsics that
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are as portable as possible, in case you need to change
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compilers in the future.
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</para>
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<para>
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This reference provides intrinsics that are guaranteed to be
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portable across compliant compilers. In particular, both the
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GCC and Clang compilers for POWER implement the intrinsics in
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this manual. The compilers may each implement many more
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intrinsics, but the ones in this manual are the only ones
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guaranteed to be portable. So if you are using an interface not
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described here, you should look for an equivalent one in this
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manual and change your code to use that.
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</para>
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<para>
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Some compilers may provide compatibility headers for use with
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other architectures. Recent GCC and Clang compilers support
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compatibility headers for the lower levels of the x86 vector
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architecture. These can be used initially for ease of porting,
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but for best performance, it is preferable to rewrite important
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sections of code with native Power intrinsics.
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There are also other vector APIs that may be of use to you (see
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<xref linkend="VIPR.techniques.apis" />). In particular, the
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POWER Vector Library (see <xref
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linkend="VIPR.techniques.pveclib" />) provides additional
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portability across compiler versions.
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</para>
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</section>
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<section>
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<title>Use Assembly Code Sparingly</title>
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<para>filler</para>
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<section>
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<title>Inline Assembly</title>
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<para>filler</para>
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</section>
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<section>
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<title>Assembly Files</title>
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<para>filler</para>
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</section>
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<para>
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Sometimes the compiler will absolutely not cooperate in giving
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you the code you need. You might not get the instruction you
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want, or you might get extra instructions that are slowing down
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your ideal performance. When that happens, the first thing you
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should do is report this to the compiler community! This will
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allow them to get the problem fixed in the next release of the
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compiler.
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</para>
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<para>
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In the meanwhile, though, what are your options? As a
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workaround, your best option may be to use assembly code. There
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are two ways to go about this. Using inline assembly is
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generally appropriate only for very small snippets of code (1-5
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instructions, say). If you want to write a whole function in
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assembly code, though, it is better to create a separate
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<code>.s</code> or <code>.S</code> file. The only difference in
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these two file types is that a <code>.S</code> file will be
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processed by the C preprocessor before being assembled.
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</para>
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<para>
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Assembly programming is beyond the scope of this manual.
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Getting inline assembly correct can be quite tricky, and it is
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best to look at existing examples to learn how to use it
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properly. However, there is a good introduction to inline
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assembly in <emphasis>Using the GNU Compiler
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Collection</emphasis> (see <xref linkend="VIPR.intro.links" />),
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in section 6.47 at the time of this writing.
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</para>
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<para>
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If you write a function entirely in assembly, you are
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responsible for following the calling conventions established by
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the ABI (see <xref linkend="VIPR.intro.links" />). Again, it is
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best to look at examples. One place to find well-written
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<code>.S</code> files is in the GLIBC project.
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</para>
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</section>
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<section>
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<section xml:id="VIPR.techniques.apis">
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<title>Other Vector Programming APIs</title>
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<para>In addition to the intrinsic functions provided in this
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reference, programmers should be aware of other vector programming
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@ -69,14 +205,13 @@ xmlns:xlink="http://www.w3.org/1999/xlink" xml:id="section_techniques">
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<section>
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<title>x86 Vector Portability Headers</title>
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<para>
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Recent versions of the <code>gcc</code> and <code>clang</code>
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open source compilers provide "drop-in" portability headers
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for portions of the Intel Architecture Instruction Set
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Extensions (see <xref linkend="VIPR.intro.links" />). These
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headers mirror the APIs of Intel headers having the same
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names. Support is provided for the MMX and SSE layers, up
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through SSE4. At this time, no support for the AVX layers is
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envisioned.
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Recent versions of the GCC and Clang open source compilers
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provide "drop-in" portability headers for portions of the
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Intel Architecture Instruction Set Extensions (see <xref
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linkend="VIPR.intro.links" />). These headers mirror the APIs
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of Intel headers having the same names. Support is provided
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for the MMX and SSE layers, up through SSE4. At this time, no
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support for the AVX layers is envisioned.
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</para>
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<para>
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The portability headers provide the same semantics as the
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@ -95,7 +230,7 @@ xmlns:xlink="http://www.w3.org/1999/xlink" xml:id="section_techniques">
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<code><mmintrin.h></code>.
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</para>
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</section>
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<section>
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<section xml:id="VIPR.techniques.pveclib">
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<title>The POWER Vector Library (pveclib)</title>
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<para>The POWER Vector Library, also known as
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<code>pveclib</code>, is a separate project available from
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Bill Schmidt
5 years ago