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Explain vector instructions types in brief.

Vector Instruction Types (Brief Overview for B.Tech CSE)

Vector instructions operate on multiple data elements in parallel and are central to SIMD/vector architectures in advanced computer systems. They accelerate tasks like image processing, scientific computing, AI, and data analytics by applying one instruction to a whole array (vector) of numbers.

1) Arithmetic and Logical Operations

  • Vector–Vector (VV): Operate element-wise on two vectors of the same length (e.g., add, sub, mul, div, AND, OR, XOR).
  • Vector–Scalar (VS): Apply a scalar to every element of a vector (useful for scaling, biasing).
  • Vector–Immediate (VI): Use a small constant encoded in the instruction for quick operations.
  • Fused/Extended: Fused multiply-add (FMA), widening (e.g., 16-bit to 32-bit), narrowing, and saturating arithmetic for fixed-point safety.
# VV
C[i] = A[i] + B[i]
# VS
C[i] = A[i] * k
# VI
C[i] = A[i] + 5
# FMA
C[i] = A[i] * B[i] + D[i]

2) Memory and Data Movement

  • Unit-Stride Load/Store: Read/write contiguous elements (fastest and most common).
  • Strided Load/Store: Access elements with a constant step (useful for matrix columns).
  • Gather/Scatter (Indexed): Load from or store to non-contiguous addresses using an index vector.
  • Segmented Loads/Stores: Move structured data records (AoS/SoA layouts).
  • Prefetch/Streaming Hints: Bring data to cache early to reduce stalls.
# Unit-stride
V = load(A)          # A[0..n-1]
store(C, V)

# Strided
V = load_strided(A, stride)

# Gather/Scatter
V = gather(A, idx)   # V[i] = A[idx[i]]
scatter(C, idx, V)

3) Comparison, Masking, and Predication

  • Vector Compare: Produces a mask vector (true/false per element) via ==, !=, <, ≤, etc.
  • Masked Operations: Execute only on elements where mask is true; others are preserved or zeroed.
  • Blend/Select: Combine two vectors based on a mask (conditional move without branching).
mask = (A[i] > B[i])
C[i] = select(mask, A[i], B[i])  # if mask[i] then A[i] else B[i]
D[i] = add(A[i], B[i]) under mask

4) Reductions (Horizontal Operations)

  • Sum/Min/Max: Reduce all elements to a single scalar.
  • Logical Reductions: AND/OR/XOR across all elements.
  • Dot Product and Accumulate: Common in ML and DSP.
  • Prefix (Scan): Inclusive/exclusive scans for parallel algorithms.
sum = reduce_sum(A)          # A[0] + A[1] + ... + A[n-1]
m   = reduce_max(A)
dot = reduce_sum(A[i]*B[i])

5) Permute, Shuffle, and Reordering

  • Shuffle/Permute: Rearrange elements based on a pattern or index vector.
  • Slide/Rotate: Shift elements left/right with fill.
  • Zip/Unzip (Interleave/Deinterleave): Useful in multimedia and matrix transposes.
  • Compress/Expand under Mask: Pack active elements or scatter them back with gaps.
V2 = shuffle(V, idx)     # V2[i] = V[idx[i]]
V3 = slide_left(V, k)
P  = interleave(A, B)    # zip
Q  = compress(V, mask)

6) Type Conversion and Packing

  • Widening/Narrowing: Convert between precisions (e.g., int16↔int32, fp16↔fp32).
  • Float↔Integer: With rounding modes and saturation options.
  • Packing/Unpacking: Pack multiple small elements into wider lanes or split them out.
I32 = widen(I16)
F32 = to_float(I32, round=nearest)
I16 = narrow_saturate(I32)

7) Vector Control/Configuration

  • Set Vector Length / Element Width: Configure how many elements are active per operation.
  • Predicate/Mask Setup: Create and manage mask registers for guarded execution.
  • Policy Controls: Tail handling and merging behavior for partial vectors.
VL = set_vector_length(n)   # activate n lanes
enable_mask(mask)

8) Specialized and Bitwise Operations

  • Bit Manipulation: Shifts, rotates, bit test/set/clear, population count.
  • Cryptographic/Pattern Ops: Useful for hashing, checksums, and security primitives.
C[i] = rotate_left(A[i], r)
cnt  = popcount_vector(A)

Typical Use Cases

  • Image and signal processing: vector add, multiply, convolutions, reductions.
  • Machine learning: dot products, activation functions with masks, mixed-precision conversions.
  • Scientific computing: vectorized loops, gather/scatter for sparse data.
  • Data analytics: filtering with masks, prefix sums, compress/expand.

In summary, vector instruction types broadly include arithmetic/logical (VV/VS/VI), memory movement (unit-stride, strided, gather/scatter), comparisons with masking, reductions, permutation/shuffle, conversions/packing, and control/configuration. Mastering these categories helps write efficient SIMD code and understand modern vector architectures.