Distributed Compute Labs is a Canadian educational non-profit organization responsible for developing the next-generation of compute networks.
Modern day research requires extensive computing power. Researchers are competing for limited resources, either in availability or cost. Meanwhile, your computer's own processing power is going to waste while it sits idle. Distributed Compute Labs is building the tools needed for you to provide your compute power to the network as well as draw compute power from the network on-demand. Organizing processing power as a utility will accelerate compute-enabled research, innovation, and discovery across the world.
Compute providers receive Distributed Compute Credits (DCC) in exchange for their compute time. The protocol is designed to soak up unused cycles in otherwise active machines, or complete usage of a dedicated machine. DCC’s can then be used to deploy new compute tasks, or sold in the global marketplace.
Researchers and developers deploy computational projects in exchange for DCC. Abundant compute resources are available at a fraction of the cost of current commercial cloud computing services, disrupting existing market powers and accelerating compute-enabled research, innovation, and discovery.
Exact mathematical solutions describing the electromagnetic force interactions arising between a two-layer ferromagnetic conducting tube and a coaxial bobbin coil, carrying a DC or AC current and traveling at constant velocity, are presented here. The research is aimed at exploring innovative electromagnetic propulsion technologies with potential applications in futuristic transportation and space elevators.
In simulating different geometries and material properties, 109 computations would require almost two years to complete using a single modern CPU core. By spreading these calculations over a million cores on the DCP network, the problem is completed in hours instead of years.
Connect your idle processing power from mobile devices to mainframes to help accelerate academic, research, institutional, and enterprise computational objectives.
Deploy compute tasks from your computer and obtain results at unprecedented speed, cost-effectiveness, and ease-of-use. Compute resources are securely sourced from a decentralized compute supply.
Dr. Kristine Spekkens
Associate Professor, RMC Physics and Space Science
Dr. Gunnar Blohm
Associate Professor, Queen’s University Computational Neuroscience
President & CEO Compute Ontario
Dr. Felicia Magpantay
Assistant Professor, Queen’s University Mathematics and Statistics
DCP consists of a task scheduling system, an algorithm for efficiently distributing those tasks, and a compartment to securely compute them. Research groups can compute together using their own equipment, and reach out to a trusted network of global providers during peak demand.
DCP will remain browser-compatible, enabling new opportunities in distributed computing. When embedded, micro-computations provide passive income from compute power, which provide an alternative to webpage advertisements or subscription costs.
Subsequent versions of the protocol will see the addition of more extensive implementation kits and auxiliary libraries that will plug into widely used and industry-leading software.
DCP is standards-based and future-proof.
It is designed to be lightweight and of the simplest form, built on top of shared standards with a similar philosophy.
Web Assembly was created for absolute performance without compromising security. Specific tasks can be hard-typed and compiled ahead of time allowing developers to benefit from the convenience and performance of both high and low-level languages. Web assembly is a compile target for many native languages including C, C++, Julia, and Rust. Dozens of other compilers are under development by their respective communities.
DCP currently integrates WebGL 2 for rendering and general compute tasks. WebCompute Shaders, the browser's CUDA equivalent, is under development. GPUs exist mostly at the edge of the network allowing DCP to gain more power than centralized GPU centers. DCP will enable GPU processing and in the future, GPGPU computation, parallel data and algorithms, physics simulations, machine learning, and neural networks.