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Vector Processing Futures

Originally published  June, 2000
by Carlo Kopp
2000, 2005 Carlo Kopp

Usually associated with multimillion dollar Cray supercomputers, vector processing is a powerful technique for many applications. In this month's issue we will look at the fundamental ideas behind this computing model, and discuss the future outlook for this technology.

The issue of vector processing frequently raises the question - does it have a future in this day and age of cheap commodity general purpose processors ? Many observers see this technique as a highly specialised computing technology which is worthless outside the domains of simulating aerodynamics, quantum physics, chemistry, supernovas, cryptography, geophysical data processing, finite element analysis in structural engineering, synthetic aperture radar image processing and other similar "heavy weight engineering / scientific" applications.

As always, reality tends to be a little more complex than the media and sales community would have us believe. Vector processing is no exception in this respect.

What is Vector Processing?

The idea of vector processing dates back to the 1960s, a period which saw massive growth in the performance and capabilities of mainframes, then the primary processing tools of the industry. Analysing the performance limitations of the rather conventional CISC style architectures of the period, it was discovered very quickly that operations on vectors and matrices were one of the most demanding CPU bound numerical computational problems faced.

A vector is a mathematical construct which is central to linear algebra, which is the foundation of much of classical engineering and scientific mathematics. The simplest physical interpretation of a vector in 3 dimensional space is that it is an entity which is described by a magnitude and a direction, which in a 3 dimensional space can be described by 3 numbers, one for x, one for y and one for z. A vector [a b c] is said to have 3 elements, and is stored in memory as an array.

Just as real numbers (float, double) and integers (int, long) can be manipulated, so vectors can be manipulated, but the operations are quite different.

  • Scaling - physically stretching or shrinking the length of the vector, is done by multiplying each element in the vector by the same scaling factor e.g. r*[a b c] == [ra rb rc].

  • Addition - creating a new vector by parallelogram addition, is done by adding the respective first, second and third elements e.g. [a b c] + [d e f] == [a+d b+e c+f].

These are the two most simple vector operations, and readers with a science education will recall that vectors follow the distributive, associative and commutative laws, so that many of the basic tricks in algebra also hold for vector arithmetic.

However, vectors have distinct geometrical interpretations as well, and this reflects in further types of operations between vectors.

  • Dot Products - a number physically representing the product of the length of two vectors multiplied by the cosine of the angle between them, performed by multiplying the respective elements and adding the products e.g. [a b c].[d e f] == ad + be + cf.

  • Cross Products - a vector perpendicular to two vectors, found by taking the differences of products of the elements e.g. [a b c]X[d e f] == [(bf-ce) (cd-af) (ae-bd)].

Needless to say, a large volume of mathematical theory has grown out of vector algebra and calculus. As programmers what interests us is the kind of structures and operations which are needed to compute such problems.

What turns out to be of particular interest are matrices and matrix operations, colloquially termed in the trade "matrix bashing". Matrices provide some very convenient ways of representing and solving systems of linear equations, or performing linear transformations (functions) on vectors (scaling and changing the direction of a vector).

A matrix in the simplest of terms is a two dimensional array of coefficients, usually floating point numbers. Matrices can be added, multiplied or scaled, and given some caveats, obey the laws of association, commutation and distribution. What is important about operations between matrices is that these operations mostly involve multiplications, additions or dot and cross products between rows and columns in the respective matrices.

This is very important from the perspective of machine arithmetic, since operations between rows and columns amount to primitive vector operations. Therefore inside the machine they can be represented with a single operation code (opcode) ad one dimensional arrays of operands.

Consider the choices we have in building hardware to compute say an addition between two vectors. On a conventional machine we must perform N individual operand loads into registers, N additions and N stores for two N sized vectors. Each load. addition and store incurs an opcode fetch cycle and all of the heartache of shuffling the operands around in the CPU.

The alternative is to add an instruction into the machine which says "ADD, N, A, B, C", or add N element vectors A and B and store the result into vector C, where operands A, B and C are pointers to the memory addresses of arrays A, B and C. Doing it this way achieves several desirable things - we use only one opcode no matter how big N is, thereby saving a lot of bus bandwidth and decoder time, and we end up with a repetitive run of identical operations, here loads, adds and stores, which means that in a pipelined CPU we minimise the number of pipeline stalls and thus lost performance. The bigger the length of the vector, the better, although in practice additional little gain is seen for vector sizes bigger than 10-20.

This is the central idea behind vector processors and why they can achieve significantly better performance than their general purpose cousins - by collapsing large numbers of basic operations into single vector operations which involve repeated bursts of identical primitive operations.

Of course, a vector processor built around the internal architecture of a general purpose pipelined CPU, which merely includes extra microcode or control logic to execute vector instructions, will more than likely deliver at most several times the performance of its mundane cousin. Even a Cray 1 delivers only about an 8 fold performance improvement between its vector and scalar hardware (actually superscalar hardware - as a minor diversion, the term superscalar would appear to have its origins in the supercomputing world, where the use of multiple execution units for scalar problems needed a decent buzzword !), on typical computations compiled using either vector or conventional/scalar operations.

To get genuinely blinding improvements in speed, it is necessary to optimise the architecture to facilitate vector operations. This amounts to organising the storage in a manner which makes it easy to quickly load and store array operands, or in other terms, the bandwidth between the CPU and main memory must be maximised, and the bandwidths between operands and execution units in the CPU must be maximised.

Various techniques have been used over the last 4 decades to achieve this. The brute force approach, is to put a very wide and fast bus between the main memory and the CPU, and use the fastest RAM you can buy. This approach is particularly attractive if you are working with very large vectors which have dozens or hundreds of elements, since the CPU can crunch away to its heart's content sucking vector elements directly into the execution unit load registers.

The cheaper approach, used in the Cray 1 and early Control Data machines, is to put a very large block of "scratch-pad" memory into the CPU as a vector operand register bank (file), or "vector registers". Since the preferred format of vector instructions uses three operands, two sources and one destination for the result, this register bank usually ends up being split into three or more parts.

To execute a vector instruction with an vector length which fits into the vector register bank, the two source operand arrays are sucked into the registers. Then the execution unit grinds its way through the instruction, leaving the result vector in the reserved area of vector register bank. It can then be copied back into memory.

This strategy works wonderfully until the vector length is larger than the vector register array length. At that point, the CPU has to chop the operation into multiple passes, since it can only work through a single register bank's worth of operands at a time. So it will go through a load cycle, vector compute cycle, store cycle, then another load cycle, etc, repeating this process until the whole vector operation is completed. Every time the CPU has to do an intermediate store and load of the vector register bank, the execution unit is idling and no work gets done. This is the classical "fragmentation" problem, since every time the vector operand overruns a multiple of the vector register array's size, the time overhead of a load and store cycle for the whole vector register array is incurred.

My favourite illustration of this is the famous curve of Cray 1 MegaFLOPS performance vs the dimensions of two matrices being multiplied. The Cray 1 has vector registers sized at 64. For a matrix size of up to about twenty, the MFLOPS shoot up almost linearly to about 80 MFLOPS, upon which the speedup slows down to 132 MFLOPS for 64x64 matrices. Then the MFLOPS dip down to about 100, since the registers have to be reloaded again, after which they creep up to 133 MFLOPS at 128x128, and this repeats on and on at multiples of 64.

The only way to work around this problem is to make the vector register banks bigger, which costs extra bucks.

Clearly the biggest performance killer in such a vector processor is the need to reload the vector register banks with operands. There do however exist situations where a particular trick, termed "chaining", can be used to work around this.

Consider a situation where you are manipulating a vector, and then manipulating the resulting vector, and so on, and the vector fits inside a vector register array. What this means is that the result vector from one vector instruction, becomes one of the input operands for a subsequent vector instructions. Why bother storing it out into memory and then loading it back into the CPU again ?

Chaining involves managing the use of the vector register arrays in the register bank in a manner which minimises the number of redundant load-store cycles on vector operands. A three vector operand instruction set (opcode, source1, source2, result) requires a minimum of three vector register arrays, so any number of additional vector register arrays above this facilitates chaining. The venerable Cray 1 used four. If your CPU is well organised internally, and uses chaining, then it is feasible to then load a vector register array with a vector operand for a subsequent instruction, while the execution unit is busily crunching away on the remaining three vector registers, doing the current instruction. Once it has finished that vector instruction, it starts the next with the two operands already sitting in the vector registers. If the following vector instruction can also be chained, then its vector operand can be loaded into the unused four vector register.

Chaining works best if the time to load a vector register array is equal or shorter than the time to execute a vector instruction. This is one of the reasons why main memory bandwidth, both in terms of RAM speed and bus speed, suffers little compromise in a supercomputer.

Getting Bang for Your Buck

Extracting value for money out of vector processing hardware depends very much upon the problem you are trying to solve, the compiler you have, and the idiosyncrasies of the vector hardware on your machine.

If your purpose in life is to deal with the "classical supercomputing" problems which involve mostly large matrix operations, then vector processors like Crays are the best approach. The basic problem is rich in primitive vector operations and the machine can be be driven to its best.

In practice, most problems involve a mix of scalar and vector computations. However,it turns out that even in solving a scalar problem the vector hardware might be used to an advantage. Instruction Level Parallelism (ILP) is the cornerstone of squeezing extra performance out of conventional superscalar processors, by concurrently executing consecutive instructions which have no mutual dependencies.

This idea can be frequently exploited in a vector processor. Consider a loop with a large number of passes, in which several computations have no mutual dependency - what is computed in one pass through the loop in no way affects any other pass. If you have a vector processor, then your compiler can cleverly arrange to compute all of the these instructions on the vector unit rather than as consecutive passes through the scalar unit. This technique for parallelising loops when possible is usually credited to the original Cray Fortran compiler.

So vector hardware is not necessarily a dead loss in areas outside its primary role. Exploiting it properly does require proper compiler support, and also a suitable language. Fortran was designed for matrix bashing and has superb support for arrays and complex numbers. C and C++, despite being well designed for system programming and many other application areas, has quite primitive support for arrays. A recent proprietary improvement has been a C compiler for Digital Signal Processing (DSP) chips, with language extensions to support complex numbers.

The native vector size of the vector processing hardware is also an issue, as noted previously. If you are to perform operations on large vectors, then a machine native vector size must also be large.

However, many situations do arise where a short vector size is perfectly adequate. The most obvious of these is in the processing of 3D graphics, where images must be rotated, scaled and translated. All of these operations map very efficiently into floating point vector operations with 3 element long vectors. Signal and image processing are other areas of interest, since they also present opportunities for vectorisation.

The central issue is however the ability of the compiler, or hand crafted assembler libraries, to transform the primitive vector operations in the application code into a format compatible with the hardware. Sometimes the vector operations are obvious, other times such as in the parallelising of loops, not so obvious.

The caveat for end users is therefore to properly understand their application and tools, before spending on a processor with vector hardware in it. A tale I have cited in the past is the user who went out and bought a "vector co-processor box" only to find that his application did not vectorise well.

Vector Processing Products

The most famous and traditionally least affordable vector processors in the market are Seymour Cray's machines. Cray was a senior machine architect at Control Data Corporation, who was heavily involved in the development of their supercomputing mainframes, which pretty much dominated the scientific supercomputing market of the mid to late sixties. Cray subsequently departed CDC and set up his own shop. His first machine was the Cray 1, which proved to be stunning success in the market. A characteristic feature of early Crays was the use of a very fast bus, and a circular machine layout around what amounted to a backplane wrapped around a cylinder. This was done to minimise the length and clock cycle time of the main bus to memory.

The Cray 1 used a combination of superscalar and vector techniques and outperformed its competitors with a then stunning 133 MFLOPS of floating point performance. It was released in 1976 and the first unit went to Los Alamos, presumably to crunch away on hydrodynamic simulations of nuclear bomb implosions.

The successor to the Cray 1 was the Cray 2, with 1.9 GigaFLOPS of performance and up to 2 GB or very fast main memory, it was released in 1985.

The Cray X-MP was the first multiprocessing Cray, released in 1982, with a modest 500 MFLOPS of performance and the Unix derived Unicos operating system. It was followed in the late eighties by the Y-MP series, which cracked the 1 GFLOPS barrier for this machine family, later models used up to 8 vector processors.

The end of the Cold War brought hard times for Cray, as the primary market, Defence Department and defence contractors, collapsed very rapidly. During the nineties, Cray was purchased by Silicon Graphics, Inc., who found that owning the famous name did not return the profits they anticipated. Earlier this year, Tera Computer Company, based in Seattle, closed a deal with SGI and acquired Cray for around US $50.00M and one million shares. Tera, with a product base in supercomputing, subsequently renamed themselves Cray Inc.

The merged product line now carries the Cray brand name, and includes the SV1, SV2, T3 and T90 series machines, merging the previous SGI product lines with the Tera product lines.

The most notable of these machines is the Scalable Vector 1 (SV1) family, which is claimed to provide 1.9 GFLOPS in a Single Streaming Processor, and 4.8 GFLOPS in a Multi-Streaming Processor. Up to 32 such systems can be clustered.

Whether the new look Cray Inc. can recapture market position and mystique of Seymour Cray's original series remains to be seen - the decade is yet young.

Another product which was popular during the early nineties, especially for embedded and military applications, was the Intel i860 microprocessor family. The i860 is not a "true" vector processing machine, with only 1 million transistors and a clock speed of 40 MHz it was a modest product by high performance computing standards. What distinguished it from its contemporaries was its heavily pipelined floating point hardware, which in its "pipelined" mode of operation could deliver up to 80 MFLOPS single precision and 60 MFLOPS double precision. Widely touted as a "Cray on a chip", the i860 ended up becoming a niche number crunching processor in various add-on boxes, and a DSP substitute in Multibus and VMEbus boards. Against the commodity microprocessors of its day, it was an exceptional performer. However the need to switch it into the "vector" pipelined mode meant that many people considered it hard to code for. Usually libraries were supplied and applications had to be ported across, unlike the elegant Cray Fortran compilers. The chip was recently obsoleted and is a legacy item.


(Images - Motorola)

The currently "kewl" item in the commodity processor market is the PowerPC G4 chip, from the IBM/Apple/Motorola consortium. This machine is being marketed as a "superscalar processor with a vector co-processor", running at twice the speed of equivalent Pentiums, to cite Apple. Given that the chip has only about 1/3 to 1/2 the number of transistors than its competitors in the Intel world have, this is indeed an ambitious claim.

The MPC7400 chip is indeed a genuine vector processing machine, with a PowerPC superscalar core extended with a "short vector" co-processor and additional AltiVec vector instructions (See Figure 1.). The AltiVec vector hardware is optimised for graphics and signal processing applications, and occupies almost one third of the die (see Figure 2.). The vector unit is 128 bits wide, and can operate on vector sizes of 4 with single precision floating point operands, or 8 with 16-bit integers, or 16 with 8-bit operands. The vector register file has 32 128-bit entries, which can be accessed as 8, 16 or 32 bit operands, equivalent to 1/4 of the original Cray 1. No less than 162 AltiVec instructions are available in the machine instruction set.

The AltiVec hardware is not intended to fill the Cray-like niche of heavy weight engineering / scientific computation, but is clearly well sized to deliver in its primary niche of graphics, multimedia and signal processing, areas which can frequently stretch current superscalar CPUs.

Apple's performance claims of "a 500 MHz G4 is 2.2 times faster than an 800 MHz P-III" upon closer examination refer specifically to operations such as FFTs, convolutions, FIR filters, dot products and array multiplications, with a peak performance gain of 5.83 times for, surprise, surprise, a 32 tap convolution operation on a 1024 element array. Other than some slightly dubious clock speed scalings, the Apple numbers are what you would expect, indeed FFT performance is almost the same for both CPUs. So unless your application uses the AltiVec instruction set generously, do not expect dramatic performance advantages !

From a technology viewpoint the AltiVec G4 shows that it is now pretty much feasible to pack the architectural equivalent of a Cray 1 on to a single die. Anybody care for a desktop 1.2 GHz Crayette?




$Revision: 1.1 $
Last Updated: Sun Apr 24 11:22:45 GMT 2005
Artwork and text 2005 Carlo Kopp


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