VMulA

VMulA is a term used in discussions of vector processing to denote a vector multiply-accumulate operation, where an element-wise product of two source vectors is added to an accumulator to produce a destination vector. The basic form applies an element-wise multiplication X[i] * Y[i] and adds the result to a running total in D[i], i.e., D[i] = D[i] + X[i] * Y[i]. In some descriptions an explicit accumulator vector A is provided, giving D[i] = A[i] + X[i] * Y[i]. Depending on the architecture, D can also serve as the accumulator, yielding D[i] += X[i] * Y[i].

VMulA exploits data parallelism by applying the same operation to multiple elements simultaneously, typically via SIMD widths of 4, 8, 16, or more. This primitive is central to workloads such as inner products, FIR and IIR filtering, convolution, matrix and tensor computations, and neural network inference. It is especially valuable where high throughput is achieved by performing many multiply-accumulate operations in parallel.

Implementation and performance considerations vary by platform. VMulA is commonly realized as a multiply-accumulate instruction or intrinsic, with latency and throughput influenced by vector width, memory bandwidth, and alignment. In practice, many architectures expose similar capabilities under different names (for example, multiply-accumulate or MAC, sometimes implemented via fused multiply-add hardware). Compiler support and instruction-set documentation govern how effectively VMulA-style operations are utilized in code.

See also: Multiply-accumulate, MAC, FMA, SIMD, DSP, neural network accelerators.