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SIMD convergence is a big issue here: how do you keep those lanes running together? If you have trivial kernels sure, but ISPC guarantees convergence across co

SIMD convergence is a big issue here: how do you keep those lanes running together? If you have trivial kernels sure, but ISPC guarantees convergence across control flow which isn’t even available in CUDA (according to ISPC docs at least). Just to be clear: I want to use Julia but without guessing about SIMD use in kernels. Until this is available for complex control flow in Julia, it’s not a game changer in my opinion. Edit: that kernel is trivial. I have nested control flow to vectorize.

stabbles 6y ago
Right, I see what you're getting at. Just taking a slightly less trivial example from Intel's docs, and superficially comparing the assembly they generate to what LoopVectorization.jl can do, I guess there is some hope at least: julia> function simple!(ys, xs) @avx for i = eachindex(xs) ys[i] = if xs[i] < 3. xs[i]^2 else sqrt(xs[i]) end end end This generates vbroadcastsd, vpcmpgtq, vmulpd, vsqrtpd, vblendvpd and vmaskmovpd AVX2 instructions (I don't have AVX512). But more complex control flow does indeed not work; the `@avx` macro errors it can't handle it at the moment.
- marmaduke 6y ago
  Thanks for the example, I am interested in trying these packages in the future, but here's a current snippet of ISPC, varying float aff[nc], xh[nn], wij[nn], x[nc]; varying int ih[nn]; uniform int t_ = t & (nl - 1); uniform float k; for (int j=0; j<nc; j++) aff[j] = 0.0f; for (int j=0; j<nn; j++) for (int i=0; i<nc; i++) aff[i] += wij[j] * shuffle(xh[j], ih[j]); for (int i=0; i<nc; i++) x[i] = 0.1f*(x[i] + x[i]*x[i]*x[i]/3.0f + k*aff[i]); for (uniform int i=0; i<nn; i++) xh[i] = insert(rotate(xh[i], 1), 0, extract(x[i/nl], i&(nl-1))); Just being able to absorb SIMD lanes into the notion of varying vs uniform makes this easy to write, not to mention SIMD operations like shuffle or rotate. In Julia or Numba or CUDA I have to index into arrays, ensure compatible data layout etc. I imagine this could be done with more macrology in Julia, but again why not use something which already works.
  - stabbles 6y ago
    > but again why not use something which already works Sure, but there's always somebody crazy enough to try to implement it :p From the fact that ISPC can generate C++ (with --emit-c++) I would think their compiler is conceptually very high-level. Since so few new concept are introduced, I wouldn't be surprised if someone would implement the same DSL in Julia at some point, just to see if one can get similar performance.
    - marmaduke 6y ago
      > ISPC can generate C++ (with --emit-c++) I would think their compiler is conceptually very high-level It's a Clang-based frontend for LLVM, so yeah, no reason Julia couldn't make use of their work. I think the main thing to tap into is the uniform vs varying support, since this drives the whole thing.

stabbles 6y ago

Right, I see what you're getting at. Just taking a slightly less trivial example from Intel's docs, and superficially comparing the assembly they generate to what LoopVectorization.jl can do, I guess there is some hope at least: julia> function simple!(ys, xs) @avx for i = eachindex(xs) ys[i] = if xs[i] < 3. xs[i]^2 else sqrt(xs[i]) end end end This generates vbroadcastsd, vpcmpgtq, vmulpd, vsqrtpd, vblendvpd and vmaskmovpd AVX2 instructions (I don't have AVX512). But more complex control flow does indeed not work; the `@avx` macro errors it can't handle it at the moment.

marmaduke 6y ago
Thanks for the example, I am interested in trying these packages in the future, but here's a current snippet of ISPC, varying float aff[nc], xh[nn], wij[nn], x[nc]; varying int ih[nn]; uniform int t_ = t & (nl - 1); uniform float k; for (int j=0; j<nc; j++) aff[j] = 0.0f; for (int j=0; j<nn; j++) for (int i=0; i<nc; i++) aff[i] += wij[j] * shuffle(xh[j], ih[j]); for (int i=0; i<nc; i++) x[i] = 0.1f*(x[i] + x[i]*x[i]*x[i]/3.0f + k*aff[i]); for (uniform int i=0; i<nn; i++) xh[i] = insert(rotate(xh[i], 1), 0, extract(x[i/nl], i&(nl-1))); Just being able to absorb SIMD lanes into the notion of varying vs uniform makes this easy to write, not to mention SIMD operations like shuffle or rotate. In Julia or Numba or CUDA I have to index into arrays, ensure compatible data layout etc. I imagine this could be done with more macrology in Julia, but again why not use something which already works.
- stabbles 6y ago
  > but again why not use something which already works Sure, but there's always somebody crazy enough to try to implement it :p From the fact that ISPC can generate C++ (with --emit-c++) I would think their compiler is conceptually very high-level. Since so few new concept are introduced, I wouldn't be surprised if someone would implement the same DSL in Julia at some point, just to see if one can get similar performance.
  - marmaduke 6y ago
    > ISPC can generate C++ (with --emit-c++) I would think their compiler is conceptually very high-level It's a Clang-based frontend for LLVM, so yeah, no reason Julia couldn't make use of their work. I think the main thing to tap into is the uniform vs varying support, since this drives the whole thing.