The Chinese Solar Machine Layer by Layer Fire in the Library The Mystery Behind Anesthesia
Not so super: A supercomputer in 1996 (top left) capable of calculating one trillion operations per second took up about 2,000 square feet and consumed 500,000 watts. Recently, Intel unveiled an 80-core research chip (bottom right) that achieves the same calculation rate but is the size of a large postage stamp and uses about 65 watts.
Intel
What will it take to put thousands of microprocessors in cell phones and laptops?
Last week, Intel announced a research project that made geeks jump with glee: the first programmable "terascale" supercomputer on a chip. The company demonstrated a single chip with 80 cores, or processors, and showed that these cores could be programmed to crunch numbers at the rate of a trillion operations per second, a measure known as a teraflop. The chip is about the size of a large postage stamp, but it has the same calculation speed as a supercomputer that, in 1996, took up about 2,000 square feet and drew about 1,000 times more power.
This research chip is one of Intel's first steps toward massively multicore technology, says Nitin Borkar, engineering manager and lab project head at Intel. The goal, he says, is to use this chip to test techniques that could make massively multicore technology faster, more energy efficient, and, most daunting, easy to program. These techniques will be "funneled into future products" that could appear, if all goes well, within five to ten years.
But nearly all engineers in the computing industry agree that making consumer computers with hundreds of cores won't be easy. In fact, many aren't even sure that it can be done. The most glaring challenge will be to find a way to completely overhaul software so that applications can take advantage of numerous cores. This includes teaching software developers how to write code for multicore machines--a task known as parallel programming--and developing new tools that allow them to code accurately and efficiently.
Researchers and visionaries are already thinking about how these supercomputer chips can best be used. Intel thinks that recognition, mining, and synthesis (RMS) applications will be key. Put together, these technologies could allow real-time language translation via cell phones, real-time video search by spoken phrase or image, and better recommendation systems for shopping, meal planning, and even health care.
To make these applications a reality, the computing industry will experience some growing pains, says David Patterson, professor of computer science at the University of California, Berkeley. (He and his colleagues have a website that hosts discussions and provides a white paper and videos on the topic.) "We're at the early stages of this gigantic change," Patterson says. He describes the direction in which the industry has decided to go--abandoning performance-constrained, single-core processors for multicore technology--as like a "Hail Mary pass" thrown in a football game. Chip makers are putting more and more cores on a chip, but the software engineers aren't sure if they can keep up. "It's an exciting time for researchers," Patterson says, "if we can figure out how to catch the pass."
Because clock frequency--the measure of processor speed--of single-core chips kept rising steadily for decades, programmers could dodge the challenge of programming in parallel, says John Shalf, computer scientist at Lawrence Berkeley Laboratory, in Berkeley, CA. Their programs would run faster if they just waited 18 months for the next generation of chip to arrive, he says. But by about 2002, it became evident that these single-core chips were consuming too much power and weren't going to be able to maintain the speed increases. So, the industry decided to change tack: instead of trying to eke out more speed from a single processor, chip makers simply added another processor. "Now that we can't crank up the clock frequency, we have to face parallelism head-on," Shalf says, "and the best way to characterize the industry's response is widespread panic."
The Promise of Personal Supercomputers
The high computing power made available in this chip can only be utilized for the application areas where it is really required. We cannot think of utilizing it for simple applications with parallel programming. All the software applications cannot be broken down into parallel modules. As the applications are written to carry out some of the real world processes, unless these processes have parallel tasks involved, it is not advantageous to apply the parallelism here. So the question of using such high powerful chip ends here. So we need to look for those areas which are not successfully computerized due to chip computing incapability so for.
The areas such as neural networks, DNA simulations, Brain models, speech recognition so on can be better verified and tested with the this chip. Any software development frame work for parallelism should be targeted to applications in such areas.
www.browsetoknow.blogspot.com
The Programmer v.s the Compiler
Teaching programmers to write parallel programs
is illogical. A programmer should not need to
do more than code a solution to a problem.
Teaching a compiler to utilize the hardware
available for running the coded solution is more
efficient.
"Write once, Run anywhere" should not just apply
to Java.
Re: The Programmer v.s the Compiler
Hope Intel's Multithread library released recently removes this multi-threading headache (?) from programmers... atleast for normal programmers.
Killer App - more accurate modelling
The thousand core chips will allow for faster and more comprehensive simulations in fields from weather forecasts, climate modelling, and my personal favourite, genetic algorithms to improve designs.
If they were to be widespread by their inclusion in mobile phones, the 'average joe' could have holographic movie projectors to watch their favourite films.
Manufacturing in the United States is in trouble. That's bad news not just for the country's economy but for the future of innovation.
This document is part of the “How-To Guide for Most Common Measurements” centralized resource portal. This tutorial provides a detailed guide for measurement and device considerations to take temperature measurements using thermocouples. Get an introduction to thermocouples, which are inexpensive sensing devices widely used with PC-based data acquisition systems. Also review some specific thermocouple examples and learn how thermocouples work and ways to integrate them into a data acquisition measurement system.
View full PDF >Our list of the 50 most innovative companies, including the following:
Gaetano Marano
246 Comments
How can we use so much power without a considerable AI improvements?
.
How can we use so much power without a considerable artificial intelligence improvements, smart ("I robot" like) humanoids and "popular" parallel-processing software?
If phones' manufacturers will put a teraflop processor in their cellular phones, the latter must be (at least) able to write and send SMS messages and do voice calls WITHOUT any human... :)
www.gaetanomarano.it/articles/articles.html
.
Reply
Guest (brianbosworthy)
Re: How can we use so much power without a considerable AI improvements?
The problem is the cascade of information from one level to the next. In the visual cortex each level computes one level of instruction. In the dolphin each level computes each instruction in the same cascade to produce the visual image from sound. We need these algorithms in our multicores to produce the image or audio meaning not in a linear single data stream, but as a cascade, for higher function.
Reply