An Overview of

GRID Computing


Rüdiger Berlich


Dr. Marcel Kunze


In the computing world there are many areas where technical development can't keep up with the demand for computational resources. Sometimes, workarounds used to overcome such deficiencies gain a life on their own and become the basis for new developments.

As an example, modern particle physics experiments, such as the upcoming LHC experiments [2] at CERN/Switzerland or the BaBar experiment at SLAC (Stanford, USA) [3] will, over the years, produce more data than can be realistically processed and stored in one location, even when using sophisticated cluster architectures. Predictions for the data production of the four LHC experiments are in the range of one Petabyte per experiment per year, or altogether a data rate of 40 GBit/s.

What's more, as the experiments evolve and particle accelerators become more sophisticated, the predicted growth in data production over the years far exceeds the predicted growth of computing power. The latter is described by Moore's Law, according to which the processing power doubles every 18 months. So a local cluster of a given size won't be able to keep up with processing this data, even if it is constantly being upgraded to the newest technology.

In such a situation, one has but two choices: One can try to find additional monetary resources to frequently increase the computing and storage power in the location where the data originates. Or, one can try to use distributed computing- and storage-resources already available in participating institutions - particle physics experiments are international by design. Single countries today cannot afford the huge cost involved in building and maintaining particle detectors and accelerators any more.

While, from a technical perspective, maintaining and administering local computing resources is of course preferable over distributed approaches, it becomes immediately clear that, in times of tight budgets, using available distributed resources is the only possible solution to the challenges imposed by modern particle physics.

It should be pointed out that the need to examine more data than can be realistically stored and processed in a single location is not particular to particle physics. Thus, an additional cost saving effect stems from the possibility to share distributed computing resources not only among physicists, but also with other research disciplines and business ventures.

The Vision

The lack of suitable, standardised solutions for distributed, large-scale computation has sparked a new research discipline, called GRID computing. The vision behind this new approach was first put forward by Ian Foster and Carl Kesselman in their book „The GRID - Blueprint for a new computing infrastructure“[4]. In short, it could be described as „Computing Power from a Plug in the wall“. In the end, one shouldn't need to care for the location where data is being processed. Really important are speed, safety, and reproducibility of results. It is this obvious analogy with the electrical power grid that has also given the name to GRID computing: You do not need to know where electrical power is being produced. All that is important to you is that it is delivered to your home in a reliable way.

Distributed Computing

Distributed Computing is not a new paradigm. But up until a few years ago networks were too slow to allow efficient use of remote resources. But as the bandwidth of high-speed WAN’s today even exceeds the bandwidth found in the internal links of commodity computers, it becomes inevitable that distributed computing is taken to a new level. It now becomes feasible to actually think of a set of computers coupled through a high-speed network as one large computational device. Or in other words: „The world is your computer“. Of course there are still limiting factors. The „speed“ of a network is a complex variable. It consists of the bandwidth (the number of bits you receive per second on one end of the network) and the latency (the amount of time it has taken these bits to travel from the source to the recipient. While you can today scale the bandwidth of a network connection to virtually any level - provided you can pay for it - there are physical limits to its latency. Obviously, data cannot travel faster than the speed of light. So there is a lower limit to the amount of time needed to transfer the data, no matter how sophisticated your network hardware is. But since this data will have to pass repeaters and routers along the way, the actual latency will be much higher than the physical limit. E.g., the latency across the USA is in the range of 50 msec. Still, this is not a very high value. As a comparison, a modern IDE hard drive with 7200 RPM has mean access times of 8.5 msec. So while latency does form a limiting factor, and will continue to do so in the foreseeable future, network latency is already in the range of, say, the mean access times of old MFM hard drives.

Overcoming Latency

While latency is a limiting factor, there do exist some possibilities to reduce its effects. One example is a semi-commercial approach by Canada-based Canarie, Inc. [7], called the Wavelength Disk Drive (WDD). The idea is to use the network itself as a storage device. An optical network is used to form a loop, i.e., data stays in the network until it is removed by some interested party. As the data doesn't need to be transferred into the network anymore – the network is the storage device - and since it is, with certain likelihood, already close to its recipient, access times to data are reduced. Unfortunately, the storage capacity of such a device is limited to a few Gigabytes. But it is still sufficient to allow the usage of Wavelength Disk Drives as cache-like devices. One could think of other ways to reduce latency, such as speculative copying of frequently used data, a technique often used in modern processors to overcome the speed difference between CPU-caches and main memory.

Requirements of distributed computing

Imagine you want to submit a compute job to the GRID. There are certain parameters you want to be sure of. First of all, you want to know that your job is submitted to a computer that fits the requirements of your program. Such requirements may include the processor type, local storage capacity for temporary files, amount of RAM and various other hardware parameters. Still, the idea behind GRID computing is that you do not need to know where your program is actually executed. So instead of choosing a machine from a list, you need to describe the requirements of your program in a way that can be understood by some GRID component responsible for choosing the target machine.

If you are handling sensitive data, you need to know that no unauthorised party can gain access to it. Likewise, the owner of the machine used to do your computation needs to know that you are using his hardware only in the way it is intended to. In short, there must be a trust relationship between the person who submits the job and the owner of the target machine. The complicated part is that these two people do not know each other and indeed should not need to have to interact in any way in order to allow the job submission.

Before program execution starts, any data needed by your program in order to do its job must be accessible to it from the target machine. Usually this means copying some data set over the network before transferring the program code. Alternatively, one could bring the program to the data rather than vice versa.

During and after the calculation you need to get access to the output created by your program, so this information needs to be transported back to you.

Last, but not least, you need to pay for the computing time you've used. The cost can vary from very tiny amounts of money to huge sums, but in any case there must be an accounting infrastructure across country and currency boundaries.

Most of these requirements of GRID computing could probably be satisfied using existing tools. E.g., With a Virtual Private Network and batch submission systems such as PBS it would be possible to submit jobs on remote machines in a secure and reliable way. But while many tools for distributed computing are available, they do not form – and, more importantly, do not intend to form – a homogeneous approach.

So the task at hand now is the creation of a standardised software infrastructure suitable to the requirements of distributed computing. Collectively, efforts to create such an infrastructure are today often referred to as „GRID computing“.

The World Wide GRID

There is a striking resemblance of GRID computing to the World Wide Web. The Web was started at CERN in late 1990 by Tim Berners-Lee as a means of efficient information exchange between physicists all over the world [5]. GRID computing aims at providing a means for efficient exchange of computing power and storage capacities between physicists and, just like the Web, it owes many of its current features to work done by computer scientists at CERN.

Just like it was the case in the beginning of the World Wide Web, there are currently many special purpose GRIDs, which usually use the Internet for data transportation. It'll take its time until these GRIDs grow together and form a World Wide GRID, but the ultimate goal is a global, standardised infrastructure for transparent execution of compute jobs across network boundaries. Mind you, we are not talking about every-day jobs here like, for example, spell checking of text documents. The tasks likely to be executed over a GRID will be huge analysis jobs like weather forecasts or simulation of particle decays. As described in the beginning, local clusters or workstations have become insufficient for such large-scale computation.

Data GRIDs vs. Computing GRIDs

So far we have offered the needs of particle physics as an explanation, why GRID Computing was started. Distributed data processing requires parallelisation. A typical particle physics analysis requires the analysis of millions of collisions of particles (or in short „events“). Usually, when processing one event, there is no need to have any information about the processing of another event. Thus, the analysis of a given set of events is an embarassingly parallel problem, as one only has to run the same analysis with a portion of the dataset on more than one compute node, and collect and assemble the results in the end. This is the typical situation found in data GRIDs, i.e. GRID environments tailored to the needs of the processing of huge data samples. In comparison, a computing GRID deals with the execution of parallel algorithms rather than the distributed processing of huge amounts of data. A computing GRID could thus be described as a „Super-Cluster“, assembled from local clusters and single machines all over the world. As we have described above, latency is the limiting factor for parallel computation. Usually parallel algorithms need to exchange data between participating compute nodes during the computation, so the degree of parallelisation and thus the speedup depend on the amount of data they need to exchange. One could argue that, for this reason, GRID techniques are more suitable to Data GRIDs than computational GRIDs, as the number of possible applications tolerant to the comparatively large latencies is limited. But we have also seen that the distinction between local machine and GRID becomes more and more blurred. Latency is today much less of an issue than it was 5 years ago and there are interesting new developments in the field of latency tolerant algorithms. So while the majority of GRID applications can be expected to be of the data GRID type, computing GRIDs will soon start to play an important role as well.


It shouldn't come as a surprise that some of the main initiatives related to GRID computing deal with the formation of high-speed networks and the provision of large clusters. Here are two of the more well-known efforts:


In GRID computing, the link between applications and the physical GRID infrastructure is provided by a „middleware“. It is the middleware's task to addresses most of the requirements of distributed computing mentioned above.

Due to the huge variety of projects, the following list can only be a subset of the middleware packages used in science:

Traditionally, in local compute clusters the task of sending information from one node to another has been handled by the Message Passing Interface. There is an implementation of MPI, called MPICH-G [19] that uses the Globus toolkit for authentication. It doesn't fall into the category of middleware, but should nevertheless be mentioned here due to its importance in computing GRIDs. Basically it allows treating a GRID environment like a local cluster, albeit with a larger latency.

GRID Initiatives

Again this can only be an incomplete list.

GRID Computing and Linux

We have seen that there is a huge variety of GRID computing projects. Open research benefits from open platforms. As an example, it can be expected that, should GRID computing be successful, the middleware will become part of the Operating System. In an ideal GRID environment, users and authors of software packages should need to know as little as possible about GRID computing, just like today an ordinary user doesn't need to know anything about networking in order to send an email. This is nowadays often referred to by the term “Invisible Computing”. In order to develop an integrated, GRID-aware OS the free availability of the source code is mandatory. This eventually makes Linux (and other Open Source Operating Systems) the ideal platform for GRID computing. Linux already has a strong position in GRID research, owing to the fact that GRID computing inherits many of its features and ideas from clustering. Linux is strong in this area.

Conclusion and Outlook

It should have become clear by now that GRID computing is still very much „work in progress“ and, in some ways, a buzz word. While, thanks to the involvement of many commercial and non-commercial institutions, it is safe to conclude that GRID computing will play an important role in the future, it is not yet possible to exactly determine all areas in which GRID computing will have an impact on society. Competing (but lower-scale) efforts such as .Net, Mono, .Gnu or OneNet might fulfil some of the roles „traditionally“ assigned to GRID computing. Possibly even the naming conventions might change and „GRID computing“ might become a catch-all term for everything related to distributed computation. The important point is that a lot of work is being invested in the moment in order to take distributed computing to the next level. Similar to the World Wide Web, this might even be the next job-engine.


[1]The “Transtec Compendium”

[2] Information about the Large Hadron Collider at CERN

[3] Information about the BaBar experiment

[4] The GRID, Blueprint for a new computing infrastructure; Ian Foster and Carl Kesselman, Morgan Kaufmann, ISBN 1-55860-475-8

[5] The web : how it all began

[6]OGSA Whitepaper - The Open GRID Services Architecture

[7] Information about the Wavelength Disk Drive

[8]Geant – a pan-european network infrastructure

[9] The Globus toolkit

[10] An effort to create the world's largest and fastest distributed infrastructure for open scientific research

[11] The Cactus middleware

[12] The Unicore middleware

[13] The Legion middleware

[14] The Global GRID Forum

[15] Homepage of the EU DataGrid

[16] Information about the CrossGrid project

[17] The GRID Physics Network

[18] The GRID for UK particle physics

[19] Homepage of MPICH-G2

The authors

From 1999 to 2001 Rüdiger Berlich was Managing Director of the UK office of SuSE Linux AG. He is now working in GRID research. (

Dr. Marcel Kunze is the head of the department of GRID computing and eScience of Forschungszentrum Karlsruhe / Germany.


The authors would like to thank Forschungszentrum Karlruhe (, Prof. Koch of Ruhr-University Bochum ( and the German Federal Ministry of Education and Research ( for their support.