mirror of
https://github.com/dotnet/skills.git
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Aditya Mandaleeka
87 lines
4.1 KiB
YAML
87 lines
4.1 KiB
YAML
name: exp-simd-vectorization
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description: Evaluates the dotnet-experimental/exp-simd-vectorization skill
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type: capability
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config:
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timeout: 3m
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stimuli:
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- name: Optimize manual min/max with TensorPrimitives
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prompt: This .NET 10 project has a method that finds the minimum and maximum values in a float array. It's used in a
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real-time sensor monitoring system to compute value ranges over sliding windows of 10K–100K readings. Please
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optimize FindMinMax for maximum throughput.
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environment:
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files:
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- src: fixtures/simd-tensor-primitives-minmax.cs
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dest: RangeCalculator.cs
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graders:
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- type: exit-success
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- type: prompt
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rubric:
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- Identifies that finding min and max of a float span is already covered by TensorPrimitives.Min and
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TensorPrimitives.Max
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- Replaces the scalar loop with TensorPrimitives.Min/Max or equivalent framework API — not manual SIMD
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- Does not introduce Vector128/Vector256/Vector512 intrinsics for operations already optimized by TensorPrimitives
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- name: Optimize manual product with TensorPrimitives
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prompt: This .NET 10 project computes the product of all elements in a float array for probability calculations. Arrays
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are 100-1000 elements. Please optimize Product for maximum throughput.
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environment:
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files:
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- src: fixtures/simd-tensor-primitives-product.cs
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dest: MathOps.cs
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graders:
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- type: exit-success
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- type: prompt
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rubric:
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- Identifies that aggregate product is already covered by TensorPrimitives.Product
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- Replaces the scalar loop with TensorPrimitives.Product — not manual SIMD
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- Does not introduce Vector128/Vector256/Vector512 intrinsics for an operation already optimized by
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TensorPrimitives
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- name: No optimization opportunity — dictionary-based lookup service
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prompt: This .NET 10 project is a product catalog lookup service that maps product codes to categories using
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dictionaries. The development team suspects it could benefit from SIMD optimization. Please analyze and optimize
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for maximum throughput.
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environment:
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files:
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- src: fixtures/simd-no-opportunity-catalog.cs
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dest: ProductCatalog.cs
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graders:
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- type: exit-success
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- type: prompt
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rubric:
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- Correctly identifies that this code has no meaningful SIMD optimization opportunity
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- Explains why SIMD is not applicable (dictionary lookups, hash-based operations, small string keys, branching
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logic)
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- Does not introduce unnecessary SIMD code that adds complexity without benefit
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- name: Optimize int array conditional increment with SIMD
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prompt: This .NET 10 project conditionally increments each element of an int array based on a threshold for counter
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updates. Arrays are 100K+ elements. Please optimize ConditionalIncrement.IncrementAbove for maximum throughput.
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environment:
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files:
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- src: fixtures/simd-conditional-increment.cs
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dest: ConditionalIncrement.cs
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graders:
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- type: output-contains
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config:
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substring: Vector
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- type: exit-success
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- type: prompt
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rubric:
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- Introduces Vector128/Vector256/Vector512 with GreaterThan comparison mask and ConditionalSelect or masked Add
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for conditional increment
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- Preserves scalar fallback for trailing elements or non-accelerated hardware
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- name: Optimize byte buffer bit reversal with SIMD
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prompt: This .NET 10 project reverses the bit order of each byte in a buffer for serial protocol decoding. Buffers are
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64KB+. Please optimize BitReverser.ReverseInPlace for maximum throughput.
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environment:
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files:
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- src: fixtures/simd-bit-reverser.cs
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dest: BitReverser.cs
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graders:
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- type: output-contains
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config:
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substring: Vector
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- type: exit-success
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- type: prompt
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rubric:
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- Introduces Vector128/Vector256/Vector512 with nibble-based Shuffle lookup tables for parallel bit reversal
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- Preserves scalar fallback for trailing elements or non-accelerated hardware
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