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- Vector(Array) Processor and its Types
- Definition of vector processing in computer architecture pdf
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- Vector Processing Computer Science Engineering (CSE) Notes | EduRev
In computing , a vector processor or array processor is a central processing unit CPU that implements an instruction set containing instructions that operate on one-dimensional arrays of data called vectors , compared to the scalar processors , whose instructions operate on single data items. Vector processors can greatly improve performance on certain workloads, notably numerical simulation and similar tasks. Vector machines appeared in the early s and dominated supercomputer design through the s into the s, notably the various Cray platforms.
The rapid fall in the price-to-performance ratio of conventional microprocessor designs led to the vector supercomputer's demise in the later s. As of [update] most commodity CPUs implement architectures that feature instructions for a form of vector processing on multiple vectorized data sets.
Vector processing techniques also operate in video-game console hardware and in graphics accelerators. Vector processing development began in the early s 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 under the control of a single master CPU.
The CPU fed a single common instruction to all of the arithmetic logic units 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. Nevertheless, it showed that the basic concept was sound, and, when used on data-intensive applications, such as computational fluid dynamics , the ILLIAC was the fastest machine in the world.
Computer Architecture and Organisation (CAO) - Notes & all
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. A computer for operations with functions was presented and developed by Kartsev in The basic ASC i. 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.
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. The vector technique was first fully exploited in by the famous Cray The vector instructions were applied between registers, which is much faster than talking to main memory.
Types of Array Processors
Whereas the STAR would apply a single operation across a long vector in memory and then move on to the next operation, the Cray design would load a smaller section of the vector into registers and then apply as many operations as it could to that data, thereby avoiding many of the much slower memory access operations. The Cray design used pipeline parallelism to implement vector instructions rather than multiple ALUs.
This allowed a batch of vector instructions to be pipelined into each of the ALU subunits, a technique they called vector chaining. Other examples followed.
Control Data Corporation tried to re-enter the high-end market again with its ETA machine, but it sold poorly and they took that as an opportunity to leave the supercomputing field entirely. In the early and mids Japanese companies Fujitsu , Hitachi and Nippon Electric Corporation 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. 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.
Although vector supercomputers resembling the Cray-1 are less popular these days, NEC has continued to make this type of computer up to the present day, [ when?
Vector(Array) Processor and its Types
Most recently, the SX-Aurora TSUBASA places the processor and either 24 or 48 gigabytes of memory on an HBM 2 module within a card that physically resembles a graphics coprocessor, but instead of serving as a co-processor, it is the main computer with the PC-compatible computer into which it is plugged serving support functions.
Modern graphics processing units GPUs include an array of shader pipelines which may be driven by compute kernels , which can be considered vector processors using a similar strategy for hiding memory latencies. In general terms, CPUs are able to manipulate one or two pieces of data at a time.
The data for A, B and C could be—in theory at least—encoded directly into the instruction. However, in efficient implementation things are rarely that simple.
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, during which the CPU traditionally would sit idle waiting for the requested data to show up. As CPU speeds have increased, this memory latency has historically become a large impediment to performance; see Memory wall. In order to reduce the amount of time consumed by these steps, 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 and more efficiently 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. The processor is 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, the processor reads a single instruction from memory, and it is simply implied in the definition of the instruction itself that the instruction will operate again on another item of data, at an address one increment 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 one 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:. 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 saving is fetching and decoding the instruction itself, which has to be done only one time instead of ten. The code itself is also smaller, which can lead to more efficient memory use.
But more than that, a vector processor may have multiple functional units adding those numbers in parallel. The checking of dependencies between those numbers is not required as a vector instruction specifies multiple independent operations. This simplifies the control logic required, and can improve performance by avoiding stalls.
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.
Definition of vector processing in computer architecture pdf
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 did:. 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.
Vector processing definitions-ACA
Including these types of instructions necessarily adds complexity to the core CPU. That complexity typically makes other instructions run slower—i. 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, in general, found in places such as weather prediction centers and physics labs, where huge amounts of data are "crunched".
Let r be the vector speed ratio and f be the vectorization ratio. It follows the achievable speed up of:. This ratio depends on the efficiency of the compilation like adjacency of the elements in memory. Various machines were designed to include both traditional processors and vector processors, such as the Fujitsu AP and AP Programming such heterogeneous machines can be difficult since developing programs that make best use of characteristics of different processors increases the programmer's burden.
It increases code complexity and decreases portability of the code by requiring hardware specific code to be interleaved throughout application code. There are different conceptual models to deal with the problem, for example using a coordination language and program building blocks programming libraries or higher order functions.
Vector Processing Computer Science Engineering (CSE) Notes | EduRev
Each block can have a different native implementation for each processor type. Users simply program using these abstractions and an intelligent compiler chooses the best implementation based on the context. From Wikipedia, the free encyclopedia. It is not to be confused with Array processing. The history of computer technology in their faces in Russian. Kiew: Firm "KIT". Parallel computing. Process Thread Fiber Instruction window Array data structure. Multiprocessing Memory coherency Cache coherency Cache invalidation Barrier Synchronization Application checkpointing.
Stream processing Dataflow programming Models Implicit parallelism Explicit parallelism Concurrency Non-blocking algorithm. Ateji PX Boost. Category: parallel computing Media related to Parallel computing at Wikimedia Commons. Processor technologies. Data dependency Structural Control False sharing. Tomasulo algorithm Reservation station Re-order buffer Register renaming. Branch prediction Memory dependence prediction. Single-core Multi-core Manycore Heterogeneous architecture. History of general-purpose CPUs Microprocessor chronology Processor design Digital electronics Hardware security module Semiconductor device fabrication Tick—tock model.
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