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Enable vectorization of TensorPrimitives.ConvertTruncating for float/double - #116895

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Pull Request Overview

This PR re-enables vectorization for the ConvertTruncating implementations of float→int, float→uint, double→ulong, and double→long by changing their Vectorizable flags from false to true.

  • Flip Vectorizable from false to true for four conversion operators
  • Removes outdated TODO comments
  • Allows JIT to use SIMD intrinsics for these conversions
Comments suppressed due to low confidence (1)

src/libraries/System.Numerics.Tensors/src/System/Numerics/Tensors/netcore/TensorPrimitives.ConvertTruncating.cs:106

  • Since vectorization is now enabled, add unit tests for the float→int, float→uint, double→ulong, and double→long truncating paths (including edge cases like NaN, ±∞, and out-of-range values) to ensure the SIMD implementation matches the scalar behavior.
 public static bool Vectorizable => true;

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Enable vectorization of TensorPrimitives.ConvertTruncating for float/double by stephentoub · Pull Request #116895 · dotnet/runtime · GitHub
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Pull Request Overview
This PR re-enables vectorization for the ConvertTruncating implementations of float→int, float→uint, double→ulong, and double→long by changing their Vectorizable flags from false to true.
  • Flip Vectorizable from false to true for four conversion operators
  • Removes outdated TODO comments
  • Allows JIT to use SIMD intrinsics for these conversions
Comments suppressed due to low confidence (1)
src/libraries/System.Numerics.Tensors/src/System/Numerics/Tensors/netcore/TensorPrimitives.ConvertTruncating.cs:106
  • Since vectorization is now enabled, add unit tests for the float→int, float→uint, double→ulong, and double→long truncating paths (including edge cases like NaN, ±∞, and out-of-range values) to ensure the SIMD implementation matches the scalar behavior.
 public static bool Vectorizable => true;

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Enable vectorization of TensorPrimitives.ConvertTruncating for float/double by stephentoub · Pull Request #116895 · dotnet/runtime · GitHub
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Pull Request Overview
This PR re-enables vectorization for the ConvertTruncating implementations of float→int, float→uint, double→ulong, and double→long by changing their Vectorizable flags from false to true.
  • Flip Vectorizable from false to true for four conversion operators
  • Removes outdated TODO comments
  • Allows JIT to use SIMD intrinsics for these conversions
Comments suppressed due to low confidence (1)
src/libraries/System.Numerics.Tensors/src/System/Numerics/Tensors/netcore/TensorPrimitives.ConvertTruncating.cs:106
  • Since vectorization is now enabled, add unit tests for the float→int, float→uint, double→ulong, and double→long truncating paths (including edge cases like NaN, ±∞, and out-of-range values) to ensure the SIMD implementation matches the scalar behavior.
 public static bool Vectorizable => true;

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Enable vectorization of TensorPrimitives.ConvertTruncating for float/double by stephentoub · Pull Request #116895 · dotnet/runtime · GitHub
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Pull Request Overview
This PR re-enables vectorization for the ConvertTruncating implementations of float→int, float→uint, double→ulong, and double→long by changing their Vectorizable flags from false to true.
  • Flip Vectorizable from false to true for four conversion operators
  • Removes outdated TODO comments
  • Allows JIT to use SIMD intrinsics for these conversions
Comments suppressed due to low confidence (1)
src/libraries/System.Numerics.Tensors/src/System/Numerics/Tensors/netcore/TensorPrimitives.ConvertTruncating.cs:106
  • Since vectorization is now enabled, add unit tests for the float→int, float→uint, double→ulong, and double→long truncating paths (including edge cases like NaN, ±∞, and out-of-range values) to ensure the SIMD implementation matches the scalar behavior.
 public static bool Vectorizable => true;

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Enable vectorization of TensorPrimitives.ConvertTruncating for float/double by stephentoub · Pull Request #116895 · dotnet/runtime · GitHub
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Pull Request Overview
This PR re-enables vectorization for the ConvertTruncating implementations of float→int, float→uint, double→ulong, and double→long by changing their Vectorizable flags from false to true.
  • Flip Vectorizable from false to true for four conversion operators
  • Removes outdated TODO comments
  • Allows JIT to use SIMD intrinsics for these conversions
Comments suppressed due to low confidence (1)
src/libraries/System.Numerics.Tensors/src/System/Numerics/Tensors/netcore/TensorPrimitives.ConvertTruncating.cs:106
  • Since vectorization is now enabled, add unit tests for the float→int, float→uint, double→ulong, and double→long truncating paths (including edge cases like NaN, ±∞, and out-of-range values) to ensure the SIMD implementation matches the scalar behavior.
 public static bool Vectorizable => true;

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Enable vectorization of TensorPrimitives.ConvertTruncating for float/double by stephentoub · Pull Request #116895 · dotnet/runtime · GitHub
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Pull Request Overview
This PR re-enables vectorization for the ConvertTruncating implementations of float→int, float→uint, double→ulong, and double→long by changing their Vectorizable flags from false to true.
  • Flip Vectorizable from false to true for four conversion operators
  • Removes outdated TODO comments
  • Allows JIT to use SIMD intrinsics for these conversions
Comments suppressed due to low confidence (1)
src/libraries/System.Numerics.Tensors/src/System/Numerics/Tensors/netcore/TensorPrimitives.ConvertTruncating.cs:106
  • Since vectorization is now enabled, add unit tests for the float→int, float→uint, double→ulong, and double→long truncating paths (including edge cases like NaN, ±∞, and out-of-range values) to ensure the SIMD implementation matches the scalar behavior.
 public static bool Vectorizable => true;

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Enable vectorization of TensorPrimitives.ConvertTruncating for float/double by stephentoub · Pull Request #116895 · dotnet/runtime · GitHub
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Enable vectorization of TensorPrimitives.ConvertTruncating for float/double - #116895
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stephentoub:reenablevectorization
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These were disabled against a PR that has already been merged.
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Enable vectorization of TensorPrimitives.ConvertTruncating for float/…
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Pull Request Overview
This PR re-enables vectorization for the ConvertTruncating implementations of float→int, float→uint, double→ulong, and double→long by changing their Vectorizable flags from false to true.
  • Flip Vectorizable from false to true for four conversion operators
  • Removes outdated TODO comments
  • Allows JIT to use SIMD intrinsics for these conversions
Comments suppressed due to low confidence (1)
src/libraries/System.Numerics.Tensors/src/System/Numerics/Tensors/netcore/TensorPrimitives.ConvertTruncating.cs:106
  • Since vectorization is now enabled, add unit tests for the float→int, float→uint, double→ulong, and double→long truncating paths (including edge cases like NaN, ±∞, and out-of-range values) to ensure the SIMD implementation matches the scalar behavior.
 public static bool Vectorizable => true;

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Enable vectorization of TensorPrimitives.ConvertTruncating for float/double by stephentoub · Pull Request #116895 · dotnet/runtime · GitHub
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Enable vectorization of TensorPrimitives.ConvertTruncating for float/double - #116895
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stephentoub merged 2 commits into
dotnet:mainfrom 
stephentoub:reenablevectorization
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Enable vectorization of TensorPrimitives.ConvertTruncating for float/double#116895
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Enable vectorization of TensorPrimitives.ConvertTruncating for float/…
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Pull Request Overview
This PR re-enables vectorization for the ConvertTruncating implementations of float→int, float→uint, double→ulong, and double→long by changing their Vectorizable flags from false to true.
  • Flip Vectorizable from false to true for four conversion operators
  • Removes outdated TODO comments
  • Allows JIT to use SIMD intrinsics for these conversions
Comments suppressed due to low confidence (1)
src/libraries/System.Numerics.Tensors/src/System/Numerics/Tensors/netcore/TensorPrimitives.ConvertTruncating.cs:106
  • Since vectorization is now enabled, add unit tests for the float→int, float→uint, double→ulong, and double→long truncating paths (including edge cases like NaN, ±∞, and out-of-range values) to ensure the SIMD implementation matches the scalar behavior.
 public static bool Vectorizable => true;

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