October 27, 2011
October has been a deadly month for computer science types. On October 5, Apple legend Steve Jobs passed away; a week later C language creator Dennis Ritchie was found dead; and this week artificial intelligence guru John McCarthy died at the age of 84.
McCarthy had his hand in many computer science advances during his long career, but is perhaps best known for creating Lisp, a programming language that is still in wide use today. Lisp also became the premier language for exploring AI, the computer science domain that McCarthy pioneered during the latter half of the 20th century.
Lisp refers to the language's focus on LISt Processing, in which both the data and instructions are represented as a linked lists. By being able to manipulate the code as data, Lisp provided the capability to create new custom syntaxes within the language. This feature was advertised as a way for programmers to design and implement "intelligent" computation systems. McCarthy said he designed Lisp so programmers could create Turing machines -- software that reflects a level of automated intelligence bounded by a set of rules.
As a big proponent of AI, McCarthy was attributed with coining the term "artificial intelligence" in 1955. A year later, he organized the first international conference on AI, bring together the early adherents to the field, including Marvin Minsky. In these early days, artificial intelligence was oversold, as even McCarthy realized, admitting that his 1958 paper, Programs with Common Sense "made projections that no one has yet fulfilled."
The other area of computer science that McCarthy is less well-known for is that of utility computing and time sharing. The idea of offering computing as a utility like electricity or water gained popularity in the 1960s, but faded, mostly due to lack of enabling technologies like fast networks and cheap computers. By the 21st century, both networks and compute capacity became commodities, leading to grid computing, and more recently, of course, cloud computing. Some attribute McCarthy's early work in this area as the foundation of the public and private cloud models in use today.
Compared to Jobs and Ritchie, McCarthy's work was much more theoretical, but it may turn out to have even broader impact on the industry. Although AI and utility computer were mostly confined to computer science research projects during most of his career, he managed to live long enough to see IBM computers beat humans at chess and then Jeopardy, and individuals to be able to buy compute cycles from a company that sells books over the internet.
In quieter times, sounding the bell of funding big science with big systems tends to resonate further than when ears are already burning with sour economic and national security news. For exascale's future, however, the time could be ripe to instill some sense of urgency....
Read more...
In a recent solicitation, the NSF laid out needs for furthering its scientific and engineering infrastructure with new tools to go beyond top performance, Having already delivered systems like Stampede and Blue Waters, they're turning an eye to solving data-intensive challenges. We spoke with the agency's Irene Qualters and Barry Schneider about..
Read more...
Large-scale, worldwide scientific initiatives rely on some cloud-based system to both coordinate efforts and manage computational efforts at peak times that cannot be contained within the combined in-house HPC resources. Last week at Google I/O, Brookhaven National Lab’s Sergey Panitkin discussed the role of the Google Compute Engine in providing computational support to ATLAS, a detector of high-energy particles at the Large Hadron Collider (LHC).
Read more...
05/10/2013 | Cleversafe, Cray, DDN, NetApp, & Panasas | From Wall Street to Hollywood, drug discovery to homeland security, companies and organizations of all sizes and stripes are coming face to face with the challenges – and opportunities – afforded by Big Data. Before anyone can utilize these extraordinary data repositories, however, they must first harness and manage their data stores, and do so utilizing technologies that underscore affordability, security, and scalability.
04/15/2013 | Bull | “50% of HPC users say their largest jobs scale to 120 cores or less.” How about yours? Are your codes ready to take advantage of today’s and tomorrow’s ultra-parallel HPC systems? Download this White Paper by Analysts Intersect360 Research to see what Bull and Intel’s Center for Excellence in Parallel Programming can do for your codes.
In this demonstration of SGI DMF ZeroWatt disk solution, Dr. Eng Lim Goh, SGI CTO, discusses a function of SGI DMF software to reduce costs and power consumption in an exascale (Big Data) storage datacenter.
The Cray CS300-AC cluster supercomputer offers energy efficient, air-cooled design based on modular, industry-standard platforms featuring the latest processor and network technologies and a wide range of datacenter cooling requirements.