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If you have been in high performance computing since its beginning in the 1950s, you have programmed in several languages during that time. During the 1950s and early 1960s, you programmed in assembly language. The constraint on memory and slow clock rates made every instruction precious. With small memories, overall program size was typically small, so assembly language was sufficient. Toward the end of the 1960s, programmers began writing more of their code in a high-level language such as FORTRAN. Writing in a high-level language made your work much more portable, reliable, and maintainable. Given the increasing speed and capacity of computers, the cost of using a high-level language was something most programmers were willing to accept. In the 1970s if a program spent a particularly large amount of time in a particular routine, or the routine was part of the operating system or it was a commonly used library, most likely it was written in assembly language.

During the late 1970s and early 1980s, optimizing compilers continued to improve to the point that all but the most critical portions of general-purpose programs were written in high-level languages. On the average, the compilers generate better code than most assembly language programmers. This was often because a compiler could make better use of hardware resources such as registers. In a processor with 16 registers, a programmer might adopt a convention regarding the use of registers to help keep track of what value is in what register. A compiler can use each register as much as it likes because it can precisely track when a register is available for another use.

However, during that time, high performance computer architecture was also evolving. Cray Research was developing vector processors at the very top end of the computing spectrum. Compilers were not quite ready to determine when these new vector instructions could be used. Programmers were forced to write assembly language or create highly hand-tuned FORTRAN that called the appropriate vector routines in their code. In a sense, vector processors turned back the clock when it came to trusting the compiler for a while. Programmers never lapsed completely into assembly language, but some of their FORTRAN started looking rather un-FORTRAN like. As the vector computers matured, their compilers became increasingly able to detect when vectorization could be performed. At some point, the compilers again became better than programmers on these architectures. These new compilers reduced the need for extensive directives or language extensions. The Livermore Loops was a benchmark that specifically tested the capability of a compiler to effectively optimize a set of loops. In addition to being a performance benchmark, it was also a compiler benchmark.

The RISC revolution led to an increasing dependence on the compiler. Programming early RISC processors such as the Intel i860 was painful compared to CISC processors. Subtle differences in the way a program was coded in machine language could have a significant impact on the overall performance of the program. For example, a programmer might have to count the instruction cycles between a load instruction and the use of the results of the load in a computational instruction. As superscalar processors were developed, certain pairs of instructions could be issued simultaneously, and others had to be issued serially. Because there were a large number of different RISC processors produced, programmers did not have time to learn the nuances of wringing the last bit of performance out of each processor. It was much easier to lock the processor designer and the compiler writer together (hopefully they work for the same company) and have them hash out the best way to generate the machine code. Then everyone would use the compiler and get code that made reasonably good use of the hardware.

The compiler became an important tool in the processor design cycle. Processor designers had much greater flexibility in the types of changes they could make. For example, it would be a good design in the next revision of a processor to execute existing codes 10% slower than a new revision, but by recompiling the code, it would perform 65% faster. Of course it was important to actually provide that compiler when the new processor was shipped and have the compiler give that level of performance across a wide range of codes rather than just one particular benchmark suite.

Questions & Answers

Do somebody tell me a best nano engineering book for beginners?
s. Reply
what is fullerene does it is used to make bukky balls
Devang Reply
are you nano engineer ?
what is the Synthesis, properties,and applications of carbon nano chemistry
Abhijith Reply
so some one know about replacing silicon atom with phosphorous in semiconductors device?
s. Reply
Yeah, it is a pain to say the least. You basically have to heat the substarte up to around 1000 degrees celcius then pass phosphene gas over top of it, which is explosive and toxic by the way, under very low pressure.
how to fabricate graphene ink ?
for screen printed electrodes ?
What is lattice structure?
s. Reply
of graphene you mean?
or in general
in general
Graphene has a hexagonal structure
On having this app for quite a bit time, Haven't realised there's a chat room in it.
what is biological synthesis of nanoparticles
Sanket Reply
what's the easiest and fastest way to the synthesize AgNP?
Damian Reply
types of nano material
abeetha Reply
I start with an easy one. carbon nanotubes woven into a long filament like a string
many many of nanotubes
what is the k.e before it land
what is the function of carbon nanotubes?
I'm interested in nanotube
what is nanomaterials​ and their applications of sensors.
Ramkumar Reply
what is nano technology
Sravani Reply
what is system testing?
preparation of nanomaterial
Victor Reply
Yes, Nanotechnology has a very fast field of applications and their is always something new to do with it...
Himanshu Reply
good afternoon madam
what is system testing
what is the application of nanotechnology?
In this morden time nanotechnology used in many field . 1-Electronics-manufacturad IC ,RAM,MRAM,solar panel etc 2-Helth and Medical-Nanomedicine,Drug Dilivery for cancer treatment etc 3- Atomobile -MEMS, Coating on car etc. and may other field for details you can check at Google
anybody can imagine what will be happen after 100 years from now in nano tech world
after 100 year this will be not nanotechnology maybe this technology name will be change . maybe aftet 100 year . we work on electron lable practically about its properties and behaviour by the different instruments
name doesn't matter , whatever it will be change... I'm taking about effect on circumstances of the microscopic world
how hard could it be to apply nanotechnology against viral infections such HIV or Ebola?
silver nanoparticles could handle the job?
not now but maybe in future only AgNP maybe any other nanomaterials
I'm interested in Nanotube
this technology will not going on for the long time , so I'm thinking about femtotechnology 10^-15
can nanotechnology change the direction of the face of the world
Prasenjit Reply
At high concentrations (>0.01 M), the relation between absorptivity coefficient and absorbance is no longer linear. This is due to the electrostatic interactions between the quantum dots in close proximity. If the concentration of the solution is high, another effect that is seen is the scattering of light from the large number of quantum dots. This assumption only works at low concentrations of the analyte. Presence of stray light.
Ali Reply
how did you get the value of 2000N.What calculations are needed to arrive at it
Smarajit Reply
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Source:  OpenStax, High performance computing. OpenStax CNX. Aug 25, 2010 Download for free at http://cnx.org/content/col11136/1.5
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