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Vector processor
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A vector processor, or array processor, is a CPU design where the instruction set includes operations that can perform mathematical operations on multiple data elements simultaneously. This is in contrast to a scalar processor which handles one element at a time using multiple instructions.
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A vector processor, or array processor, is a CPU design where the instruction set includes operations that can perform mathematical operations on multiple data elements simultaneously. This is in contrast to a scalar processor which handles one element at a time using multiple instructions. The vast majority of CPUs are scalar (or close to it). Vector processors were common in the scientific computing area, where they formed the basis of most supercomputers through the 1980s and into the 1990s, but general increases in performance and processor design saw the near disappearance of the vector processor as a general-purpose CPU.
Today most commodity CPU designs include single instructions for some vector processing on multiple (vectorised) data sets, typically known as SIMD (Single Instruction, Multiple Data), common examples include SSE and AltiVec. Modern video game consoles and consumer computer-graphics hardware rely heavily on vector processing in their architecture. In 2000, IBM, Toshiba and Sony collaborated to create the Cell processor, consisting of one scalar processor and eight vector processors, which found use in the Sony PlayStation 3 among other applications.
Other CPU designs may include some multiple instructions for vector processing on multiple (vectorised) data sets, typically known as MIMD (Multiple Instruction, Multiple Data), such designs are very special and delicate breeds for dedicated purpose and these are not commonly marketed for general purpose applications.
The more advanced approach is not the active multiplicity of instructions in parallel but the active multiplicity in sequence, which led to the pipelining concept.
History
Vector processing was first worked on in the early 1960s at Westinghouse in their Solomon project. Solomon's goal was to dramatically increase math performance by using a large number of simple math co-processors (or ALUs) under the control of a single master CPU. The CPU fed a single common instruction to all of the ALUs, one per "cycle", but with a different data point for each one to work on. This allowed the Solomon machine to apply a single algorithm to a large data set, fed in the form of an array. In 1962 Westinghouse cancelled the project, but the effort was re-started at the University of Illinois as the ILLIAC IV. Their version of the design originally called for a 1 GFLOPS machine with 256 ALUs, but when it was finally delivered in 1972 it had only 64 ALUs and could reach only 100 to 150 MFLOPS. Nevertheless it showed that the basic concept was sound, and when used on data-intensive applications, such as computational fluid dynamics, the "failed" ILLIAC was the fastest machine in the world. It should be noted that the ILLIAC approach of using separate ALUs for each data element is not common to later designs, and is often referred to under a separate category, massively parallel computing.
The first successful implementation of vector processing appears to be the CDC STAR-100 and the Texas Instruments Advanced Scientific Computer (ASC). The basic ASC (i.e., "one pipe") ALU used a pipeline architecture which supported both scalar and vector computations, with peak performance reaching approximately 20 MFLOPS, readily achieved when processing long vectors. Expanded ALU configurations supported "two pipes" or "four pipes" with a corresponding 2X or 4X performance gain. Memory bandwidth was sufficient to support these expanded modes. The STAR was otherwise slower than CDC's own supercomputers like the CDC 7600, but at data related tasks they could keep up while being much smaller and less expensive. However the machine also took considerable time decoding the vector instructions and getting ready to run the process, so it required very specific data sets to work on before it actually sped anything up.
vector instructions were applied between registers, which is much faster than talking to main memory. The Cray design used pipeline parallelism to implement vector instructions rather than multiple ALUs.
In addition the design had completely separate pipelines for different instructions, for example, addition/subtraction was implemented in different hardware than multiplication. This allowed a batch of vector instructions themselves to be pipelined, a technique they called vector chaining. The Cray-1 normally had a performance of about 80 MFLOPS, but with up to three chains running it could peak at 240 MFLOPS – a respectable number even as of 2002.
Other examples followed. CDC tried to re-enter the high-end market again with its ETA-10 machine, but it sold poorly and they took that as an opportunity to leave the supercomputing field entirely. Various Japanese companies (Fujitsu, Hitachi and NEC) introduced register-based vector machines similar to the Cray-1, typically being slightly faster and much smaller. Oregon-based Floating Point Systems (FPS) built add-on array processors for minicomputers, later building their own minisupercomputers. However Cray continued to be the performance leader, continually beating the competition with a series of machines that led to the Cray-2, Cray X-MP and Cray Y-MP. Since then the supercomputer market has focused much more on massively parallel processing rather than better implementations of vector processors. However, recognising the benefits of vector processing IBM developed Virtual Vector Architecture for use in supercomputers coupling several scalar processors to act as a vector processor.
Today the average computer at home crunches as much data watching a short video as did all of the supercomputers in the 1970s. Vector processor elements have since been added to almost all modern CPU designs, although they are typically referred to as SIMD. In these implementations the vector processor runs beside the main scalar CPU, and is fed data from programs that know it is there.
Description
In general terms, CPUs are able to manipulate one or two pieces of data at a time. For instance, many CPUs have an instruction that essentially says "add A to B and put the result in C," while others such as the MOS 6502 require two or three instructions to perform these types of operations.
The data for A, B and C could be—in theory at least—encoded directly into the instruction. However things are rarely that simple. In general the data is rarely sent in raw form, and is instead "pointed to" by passing in an address to a memory location that holds the data. Decoding this address and getting the data out of the memory takes some time. As CPU speeds have increased, this memory latency has historically become a large impediment to performance.
In order to reduce the amount of time this takes, most modern CPUs use a technique known as instruction pipelining in which the instructions pass through several sub-units in turn. The first sub-unit reads the address and decodes it, the next "fetches" the values at those addresses, and the next does the math itself. With pipelining the "trick" is to start decoding the next instruction even before the first has left the CPU, in the fashion of an assembly line, so the address decoder is constantly in use. Any particular instruction takes the same amount of time to complete, a time known as the latency, but the CPU can process an entire batch of operations much faster than if it did so one at a time.
Vector processors take this concept one step further. Instead of pipelining just the instructions, they also pipeline the data itself. They are fed instructions that say not just to add A to B, but to add all of the numbers "from here to here" to all of the numbers "from there to there". Instead of constantly having to decode instructions and then fetch the data needed to complete them, it reads a single instruction from memory, and "knows" that the next address will be one larger than the last. This allows for significant savings in decoding time.
To illustrate what a difference this can make, consider the simple task of adding two groups of 10 numbers together. In a normal programming language you would write a "loop" that picked up each of the pairs of numbers in turn, and then added them. To the CPU, this would look something like this:
execute this loop 10 times
read the next instruction and decode it
fetch this number
fetch that number
add them
put the result here
end loop
But to a vector processor, this task looks considerably different:
read instruction and decode it
fetch these 10 numbers
fetch those 10 numbers
add them
put the results here
There are several savings inherent in this approach. For one, only two address translations are needed. Depending on the architecture, this can represent a significant savings by itself. Another savings is fetching and decoding the instruction itself, which only has to be done one time instead of ten. The code itself is also smaller, which can lead to more efficient memory use.
But more than that, the vector processor typically has some form of superscalar implementation, meaning there is not one part of the CPU adding up those 10 numbers, but perhaps two or four of them. Since the output of a vector command does not rely on the input from any other, those two (for instance) parts can each add five of the numbers, thereby completing the whole operation in half the time.
As mentioned earlier, the Cray implementations took this a step further, allowing several different types of operations to be carried out at the same time. Consider code that adds two numbers and then multiplies by a third; in the Cray these would all be fetched at once, and both added and multiplied in a single operation. Using the pseudocode above, the Cray essentially did:
read instruction and decode it
fetch these 10 numbers
fetch those 10 numbers
fetch another 10 numbers
add and multiply them
put the results here
The math operations thus completed far faster overall, the limiting factor being the time required to fetch the data from memory.
Not all problems can be attacked with this sort of solution. Adding these sorts of instructions necessarily adds complexity to the core CPU. That complexity typically makes other instructions run slower—i.e., whenever it is not adding up many numbers in a row. The more complex instructions also add to the complexity of the decoders, which might slow down the decoding of the more common instructions such as normal adding.
In fact vector processors work best only when there are large amounts of data to be worked on. For this reason, these sorts of CPUs were found primarily in supercomputers, as the supercomputers themselves were generally found in places such as weather prediction centres and physics labs, where huge amounts of data are "crunched".
See also
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