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SIMD lets one operation act on several data values at once. In Mojo, the SIMD type makes that vector explicit: its type specifies both the element type and the number of lanes, and supported operations are applied lane by lane. That expresses vector work, but it does not guarantee a speedup; performance depends on the hardware, workload, compiler, and measured result.
What SIMD means
SIMD stands for “single instruction, multiple data.” A processor can apply an operation to multiple values in parallel using vector instructions. Instead of describing a separate addition for each item, vector code describes an addition across several corresponding items.
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Mojo represents this fixed-size vector with the standard-library type SIMD[dtype, width]. The dtype identifies what each lane contains; the width is the number of lanes. Both are part of the type, not runtime metadata. The width must be a power of two, as specified in the Mojo SIMD type reference.
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How Mojo applies an operation across lanes
For a supported operation, Mojo pairs corresponding lanes and applies the operation to each pair. For example, multiplying two four-lane vectors produces four products:
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var a = SIMD[DType.int32, 4](1, 2, 3, 4)
var b = SIMD[DType.int32, 4](5, 6, 7, 8)
var products = a * bThe resulting lanes are 5, 12, 21, and 32. This is elementwise multiplication, not a dot product or matrix multiplication. The Mojo operators reference documents arithmetic operators for numeric SIMD values and bitwise operators for integral or boolean vectors; matrix multiplication is not one of the supported arithmetic operations described there.
Operands must have compatible types
For the documented arithmetic operators, the operands need matching dtypes and vector sizes. Mojo does not automatically promote a lower-precision SIMD value to a higher-precision one. When the types differ, explicitly cast to the intended dtype before operating on the vectors. Also check that the operation is supported for the selected dtype; the available operators vary by type.
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What the SIMD type says about lanes and scalar values
SIMD[DType.float32, 4] describes four 32-bit floating-point lanes. The type tells the compiler the element format and vector width at compile time. It does not, by itself, promise that the value corresponds one-to-one with a particular physical register.
A one-lane SIMD value is a Scalar. Mojo’s fixed-width scalar names, such as Float32, are aliases for one-lane SIMD types. Scalar and vector values therefore share the same numeric type foundation, while their lane counts differ.
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How to choose a SIMD width
Width is a compile-time choice, not a universal setting to maximize. The numeric-types reference gives examples such as four, eight, or 16 values being processed in parallel on modern CPUs, and connects SIMD[DType.float32, 4] and SIMD[DType.float32, 16] with 128-bit and 512-bit vectors. These are illustrative technical examples, not performance measurements or recommendations for every processor. The same reference documents a compile-time width limit of 2^15 (32,768) elements; that limit is not a practical hardware vector width.
Practical vector widths are constrained by target hardware and by the work being done. A width larger than a target’s useful native vector capability may not behave as expected, and increasing width does not necessarily improve speed. Modular’s Mojo numeric types reference advises: “Always benchmark to find the optimal width for your workload and target hardware.”
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Compare implementations on the same workload
When assessing a scalar expression against a vector expression, consider the lane count, dtype, and whether the operation is supported for that dtype. Then benchmark both on the intended target hardware using the workload that matters. SIMD syntax expresses the opportunity for vector processing; actual speed depends on the compiler’s lowering, the processor, and the workload. The official references establish no universal speedup or best width.
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For larger datasets or compute-intensive kernels, Mojo’s algorithm package provides vectorization, parallelization, and reduction primitives. These tools address broader data-parallel work than a single fixed-width SIMD expression. The algorithm package documentation positions them for larger or compute-intensive operations and notes that ordinary loops may be simpler for small elementwise tasks.
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Use a direct SIMD value when you need to express a fixed-size vector operation explicitly. Consider the algorithm primitives when the task involves processing a larger collection, distributing work, or reducing results; choose based on the shape and scale of the operation rather than assuming the more elaborate form is faster.
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