Re: [webkit-dev] SIMD support in JavaScript
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). 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. In contrast, the proposed SIMD.js has the property that code using it will not depend on expensive compiler analysis in the JIT, and is much more likely to deliver predictable performance in practice between different JIT implementations and across a very practical variety of hardware architectures. To summarize, SIMD.js will not provide a portable performance solution because vector instruction sets are sparse and vary between architectures and generations. Emscripten should not generate vector instructions because it can’t model the target machine. SIMD.js will not make use of modern SIMD features such as predication or scatter/gather. Vectorization is a compiler code generation problem that should be solved by JIT compilers, and not by the language itself. JIT compilers should continue to evolve and to start vectorizing code like modern compilers. As I mentioned above, performance portability is actually one of SIMD.js's core strengths. I have found it useful to think of the API propsed in SIMD.js as a short vector API. It hits a sweet spot, being a convenient size for many XYZW and RGB/RGBA and similar algorithms, being implementable on a wide variety of very relevant hardware architectures, being long enough to deliver worthwhile speedups for many tasks, and being short enough to still be convenient to manipulate. I agree that the short vector model doesn't address all
Re: [webkit-dev] SIMD support in JavaScript
Could this thread maybe be moved to es-discuss? ___ webkit-dev mailing list webkit-dev@lists.webkit.org https://lists.webkit.org/mailman/listinfo/webkit-dev
Re: [webkit-dev] SIMD support in JavaScript
You are free to start such a discussion on es-discuss. I think it's useful for the webkit community to be able to discuss what we think of the feature. -Filip On Sep 28, 2014, at 8:39 AM, Anne van Kesteren ann...@annevk.nl wrote: Could this thread maybe be moved to es-discuss? ___ webkit-dev mailing list webkit-dev@lists.webkit.org https://lists.webkit.org/mailman/listinfo/webkit-dev ___ webkit-dev mailing list webkit-dev@lists.webkit.org https://lists.webkit.org/mailman/listinfo/webkit-dev
Re: [webkit-dev] SIMD support in JavaScript
We will probably bring it up on es-discuss once we’ve had a chance to discuss it among WebKit folks. - Maciej On Sep 28, 2014, at 8:39 AM, Anne van Kesteren ann...@annevk.nl wrote: Could this thread maybe be moved to es-discuss? ___ webkit-dev mailing list webkit-dev@lists.webkit.org https://lists.webkit.org/mailman/listinfo/webkit-dev ___ webkit-dev mailing list webkit-dev@lists.webkit.org https://lists.webkit.org/mailman/listinfo/webkit-dev
Re: [webkit-dev] SIMD support in JavaScript
Dan, you say that SIMD.js delivers performance portability, and Nadav says it doesn’t. Nadav’s argument seems to come down to (as I understand it): - The set of vector operations supported on different CPU architectures varies widely. - 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.” - Even when vector intrinsics are supported by the CPU, whether it is profitable to use them may depend in non-obvious ways on exact characteristics of the target CPU and the surrounding code (the Port5 example). For these reasons, Nadav says that it’s better to autovectorize, and that this is the norm even for languages with explicit vector data. In other words, he’s saying that SIMD.js will result in code that is not performance-portable between different CPUs. I don’t see a rebuttal to any of these points. Instead, you argue that, because SIMD.js does not require advanced compiler analysis, it is more likely to give similar results between different JITs (presumably when targeting the same CPU, or ones with the same supported vector operations and similar perf characteristics). That seems like a totally different sense of performance portability. Given these arguments, it’s possible that you and Nadav are both right[*]. That would mean that both these statements hold: (a) SIMD.js is not performance-portable between different CPU architectures and models. (b) SIMD.js is performance-portable between different JITs targeting the same CPU model. On net, I think that combination would be a strong argument *against* SIMD.js. The Web aims for portability between different hardware and not just different software. At Apple alone we support four major CPU instruction sets and a considerably greater number of specific CPU models. From our point of view, code that is performance-portable between JITs but not between CPUs would not be good enough, and it might be actively bad if it results in worse performance on some of our CPU architectures. The WebKit community as a whole supports even more target CPU architectures. Do you agree with the above assessment? Alternately, do you have an argument that SIMD.js is performance-portable between different CPU architectures? Regards, Maciej [*] I’m not totally convinced about your argument for cross-JIT performance portability. It seems to me that, in the case of the Port5 problem, different JITs could have different levels of Port5 contention, so you would not get the same results. But let’s grant it for the sake of argument. 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). 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
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
