"APL's array operations are also ideal for implementation with SIMD, or "single instruction, multiple data", operations, that perform a single action on several different values. In some cases, such as scalar functions, the primitives are SIMD operations; in others such as Reverse, they are easily implemented using SIMD—for Reverse, SIMD selection or "shuffle". While experimental SIMD machines (such as the APL-influenced CDC Star-100) were created as early as the 1960s, SIMD computing first entered the personal computing mainstream in the 1990s and has steadily grown in prominence for high-performance computing since then. In APL, CPU vector instruction sets such as Intel's SSE are the most often way to access SIMD optimization, although Co-dfns instead runs on a GPU to attain much higher throughput at the cost of increased overhead and restriction of available algorithms."