Brief
Golem Network Token (GNT) is a cryptographic protocol that enables acquiring/lending computing power from/to another system. The ultimate objective is to utilize computing power from idle processing devices. This helps people in need of extra processing power obtain the same without spending on a hardware upgrade. In an age where computers are a vital part of our day to day lives, the importance of such a service cannot be emphasized enough. Not everybody is well-off enough to buy high-end hardware, but this shouldn’t be a restraint for them to develop a skill or pursue a career that they want.
Like most blockchain based economies, new tokens come into existence through mining of blocks. The confirmation of transactions is based on mining so it is essential to the ecosystem. The network is routed through the Golem Supercomputer, which is not centralized, but is the main source for transfer of computational power. Golem runs on a ‘proof of stake’ consensus mechanism which means a miner has a higher chance of being selected to mine a block, if they stake a large amount of GNT. Golem runs on Ethereum but it is not an ERC-20 token. It has the security of the Ethereum blockchain but has taken its own direction on a functional level.
Simple Breakdown
To break it down, here’s an example of how the Golem network functions. ‘A’ wants to build a simulation of a particular scenario. They have the requisite data to build this simulation but lack computational resources to achieve this task without overloading their computer’s processor. Through Golem, they are able to successfully execute this simulation. The Golem network deduces that 3 computers’ worth of power will be required to accomplish this task. Depending on the amount of power drawn, ‘A’ has to pay whatever the total amount comes to and the amount is split between the 3 computers. By doing this, the computers which are idol or have surplus computing power to the owner’s requirements are able to earn money for their processing. ‘A’ who was in need of computational power in order to execute a task was able to do so without significant expenditure on hardware upgrades. Golem is being integrated into many software clients by developers. This is done for a variety of reasons but mostly to pull computational power as and when necessary.
Use Cases
The most popular and widely adopted use cases for Golem are as given:
• Photogrammetry: This is a process in which the size of a geographical area is determined through a bird’s eye view picture. For example, you could take a picture of a road taken from a helicopter. Using the right methods, photogrammetry can estimate the real length of the road from just the picture. This is done through analyzing structures around the road and their real dimensions. This is then proportioned with their picture size and the road’s picture size to estimate actual length. This is a complex function that requires a lot of computational power. This could be a potential adoption for a data scientist or statistician looking at population per sq/mtr analytics or
• Video FX and CGI Rendering: This is a well-known function, it involves editing and adding additional frames, physical matter, or effects to a video or picture. For example, a movie like Transformers requires heavy CGI and VFX to add the robots (Autobots and Decepticons). This is done by large studios so it wouldn’t be an issue for them. For small time filmmakers and content creators/designers who don’t have much capital, Golem comes in handy by allowing them to implement these functions at a much lower cost.
• Testing machine learning parameters: Machine learning is basically feeding an algorithm to a computer with certain input with the goal of achieving a specified output. The machine applies the algorithm and learns from it, eventually perfecting the algorithm by keeping the output needs intact and learning from implementation mistakes. The machine even has the ability to change and tweak parts of the algorithm to make it more effective and efficient. This requires quite a lot of power. So a data scientist who uses algorithms to mine data might want to implement machine learning to segregate noise from significant information. But in order to test the parameters of the algorithm, they would require sufficient computing resources. A small time data scientist would find an impeccable solution in Golem to perform these tasks at a minimal cost.
Key Points
• Computing needs are growing day by day and the utility of a platform like Golem helps to tap into this market to connect parties with surplus computing power with parties who have a deficit.
• Data is not necessarily secured. This has been addressed by the developers themselves. The data sent to other computers cannot be encrypted or hashed when sent because it needs to be edited or reformed in its original form i.e. it’s a direct data utilization. This can be solved by implementing Zero Knowledge Proofs (ZKP). As of now, it hasn’t been done because it’s expensive but it is something the development community has hinted towards.
• It can be integrated with software to aid developers in efficiency and smooth execution. Golem computing power can also be used to host websites and servers. As of now, it is not done on a wide scale but in my opinion, this will be one of the key drivers of Golem adoption.
• It is a lot more secure than alternative distributed computing projects. On the other hand, Golem definitely does not have the capacity to stand up to Amazon Web Services and Microsoft Azure, but with the rapid development of the space and potential of Golem as real competition, it’s exciting to see how it all unfolds.
Image and Information Sources
• Golem Website
•Golem Whitepaper
•Sub-reddit for the Golem Project
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-- AB
This post is a part of the ongoing Crypto Analysis Series
Previous posts:
- Bitcoin Technicals: In Depth Breakdown of the Current Move
- The Scalability Trilemma
- Online Retailer’s Incentives for using cryptocurrencies as a medium of exchange
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