Thanks for sharing your analysis on webkit-dev.
There has been a lot of criticisms about SIMD.js this year. It is great
to read about solutions for vectorization without the problems of SIMD.js.
On 9/26/14, 3:16 PM, Nadav Rotem wrote:
havesuggested http://www.2ality.com/2013/12/simd-js.htmladding SIMD
thoughts about this proposal and to start a technical discussion about
SIMD.js support in Webkit. I BCCed some of the authors of the proposal
to allow them to participate in this discussion.
Modern processors feature SIMD (Single Instruction Multiple Data)
http://en.wikipedia.org/wiki/SIMD instructions, which perform the same
arithmetic operation on a vector of elements. SIMD instructions are used
to accelerate compute intensive code, like image processing algorithms,
because the same calculation is applied to every pixel in the image. A
single SIMD instruction can process 4 or 8 pixels at the same time.
Compilers try to make use of SIMD instructions in an optimization that
is called vectorization.
The SIMD.js API
http://wiki.ecmascript.org/doku.php?id=strawman:simd_number adds new
types, such as float32x4, and operators that map to vector instructions
on most processors. The idea behind the proposal is that manual use of
vector instructions, just like intrinsics in C, will allow developers to
developed the LLVM vectorizer
http://llvm.org/docs/Vectorizers.html and worked on a vectorizing
compiler for a data-parallel programming language. Based on my
experience with vectorization, I believe that the current proposal to
to utilize SIMD instructions. In this email I argue that vector types
Vector instruction sets are sparse, asymmetrical, and vary in size and
features from one generation to another. For example, some Intel
processors feature 512-bit wide vector instructions
means that they can process 16 floating point numbers with one
instruction. However, today’s high-end ARM processors feature 128-bit
wide vector instructions
http://www.arm.com/products/processors/technologies/neon.php and can
only process 4 floating point elements. ARM processors support
byte-sized blend instructions but only recent Intel processors added
support for byte-sized blends. ARM processors support variable shifts
but only Intel processors with AVX2 support variable shifts. Different
generations of Intel processors support different instruction sets with
different features such as broadcasting from a local register, 16-bit
and 64-bit arithmetic, and varied shuffles. Modern processors even
feature predicated arithmetic and scatter/gather instructions that are
very difficult to model using target independent high-level intrinsics.
The designers of the high-level target independent API should decide if
they want to support the union of all vector instruction sets, or the
intersection. A subset of the vector instructions that represent the
intersection of all popular instruction sets is not useable for writing
non-trivial vector programs. And the superset of the vector instructions
will cause huge performance regressions on platforms that do not support
the used instructions.
Code that uses SIMD.js is not performance-portable. Modern vectorizing
compilers feature complex cost models and heuristics for deciding when
to vectorize, at which vector width, and how many loop iterations to
interleave. The cost models takes into account the features of the
vector instruction set, properties of the architecture such as the
number of vector registers, and properties of the current processor
generation. Making a poor selection decision on any of the vectorization
parameters can result in a major performance regression. Executing
vector intrinsics on processors that don’t support them is slower than
executing multiple scalar instructions because the compiler can’t always
generate efficient with the same semantics.
I don’t believe that it is possible to write non-trivial vector code
that will show performance gains on processors from different families.
Executing vector code with insufficient hardware support will cause
major performance regressions. One of the motivations for SIMD.js was to