Re: [webkit-dev] SIMD support in JavaScript
Hi Dan! On Sep 28, 2014, at 6:44 AM, Dan Gohman sunf...@mozilla.com wrote: Hi Nadav, I agree with much of your assessment of the the proposed SIMD.js API. However, I don't believe it's unsuitability for some problems invalidates it for solving other very important problems, which it is well suited for. Performance portability is actually one of SIMD.js' biggest strengths: it's not the kind of performance portability that aims for a consistent percentage of peak on every machine (which, as you note, of course an explicit 128-bit SIMD API won't achieve), it's the kind of performance portability that achieves predictable performance and minimizes surprises across machines (though yes, there are some unavoidable ones, but overall the picture is quite good). There is a tradeoff between the performance portability of the SIMD.js ISA and its usefulness. A small number of instructions (that only targets 32bit data types, no masks, etc) is not useful for developing non-trivial vector programs. You need 16bit vector elements to support WebGL vertex indices, and lane-masking for implementing predicated control flow for programs like ray tracers. Introducing a large number of vector instructions will expose the performance portability problems. I don’t believe that there is a sweet spot in this tradeoff. I don’t think that we can find a small set of instructions that will be useful for writing non-trivial vector code that is performance portable. On 09/26/2014 03:16 PM, Nadav Rotem wrote: So far, I’ve explained why I believe SIMD.js will not be performance-portable and why it will not utilize modern instruction sets, but I have not made a suggestion on how to use vector instructions to accelerate JavaScript programs. Vectorization, like instruction scheduling and register allocation, is a code-generation problem. In order to solve these problems, it is necessary for the compiler to have intimate knowledge of the architecture. Forcing the compiler to use a specific instruction or a specific data-type is the wrong answer. We can learn a lesson from the design of compilers for data-parallel languages. GPU programs (shaders and compute languages, such as OpenCL and GLSL) are written using vector instructions because the domain of the problem requires vectors (colors and coordinates). One of the first thing that data-parallel compilers do is to break vector instructions into scalars (this process is called scalarization). After getting rid of the vectors that resulted from the problem domain, the compiler may begin to analyze the program, calculate profitability, and make use of the available instruction set. I believe that it is the responsibility of JIT compilers to use vector instructions. In the implementation of the Webkit’s FTL JIT compiler, we took one step in the direction of using vector instructions. LLVM already vectorizes some code sequences during instruction selection, and we started investigating the use of LLVM’s Loop and SLP vectorizers. We found that despite nice performance gains on a number of workloads, we experienced some performance regressions on Intel’s Sandybridge processors, which is currently a very popular desktop processor. JavaScript code contains many branches (due to dynamic speculation). Unfortunately, branches on Sandybridge execute on Port5, which is also where many vector instructions are executed. So, pressure on Port5 prevented performance gains. The LLVM vectorizer currently does not model execution port pressure and we had to disable vectorization in FTL. In the future, we intend to enable more vectorization features in FTL. This is an example of a weakness of depending on automatic vectorization alone. High-level language features create complications which can lead to surprising performance problems. Compiler transformations to target specialized hardware features often have widely varying applicability. Expensive analyses can sometimes enable more and better vectorization, but when a compiler has to do an expensive complex analysis in order to optimize, it's unlikely that a programmer can count on other compilers doing the exact same analysis and optimizing in all the same cases. This is a problem we already face in many areas of compilers, but it's more pronounced with vectorization than many other optimizations. I agree with this argument. Compiler optimizations are unpredictable. You never know when the register allocator will decide to spill a variable inside a hot loop. or a memory operation confuse the alias analysis. I also agree that loop vectorization is especially sensitive. However, it looks like the kind of vectorization that is needed to replace SIMD.js is a very simple SLP vectorization http://llvm.org/docs/Vectorizers.html#the-slp-vectorizer (BB vectorization). It is really easy for a compiler to combine a few scalar arithmetic operations into a vector. LLVM’s SLP-vectorizer
[webkit-dev] SIMD support in JavaScript
Recently members of the JavaScript community at Intel and Mozilla have suggested http://www.2ality.com/2013/12/simd-js.html adding SIMD types to the JavaScript language. In this email would like to share my 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 accelerate common compute-intensive JavaScript applications. The idea of using SIMD instructions to accelerate JavaScript code is compelling because high performance applications in JavaScript are becoming very popular. Before I became involved with JavaScript through my work on the FTL project https://www.webkit.org/blog/3362/introducing-the-webkit-ftl-jit/, I 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 include SIMD types in the JavaScript language is not the right approach to utilize SIMD instructions. In this email I argue that vector types should not be added to the JavaScript language. 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 https://software.intel.com/en-us/blogs/2013/avx-512-instructions. This 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 allow Emscripten https://developer.mozilla.org/en-US/docs/Mozilla/Projects/Emscripten to vectorize C code and to emit JavaScript SIMD intrinsics. One problem with this suggestion is that the Emscripten compiler should not be assuming that the target is an x86 machine and that a specific vector width and interleave width is the right answer.
