Rendering income

To ensure the stability of the growth rate of the token supply, we adopt a token design scheme with fixed block rewards; that is, a fixed number of tokens will be generated for rendering tasks within each unit of time to reward miners engaged in cloud rendering. The network will allocate different numbers of tokens to each miner in the current unit of time as rendering rewards based on the GPU performance, video memory, memory, vCPU performance, maximum bandwidth of different graphics cards used by miners when running rendering tasks, as well as the duration of the rendering tasks executed.

Selection of independent variables for rendering income formula: GPU performance, video memory, memory, vCPU performance, maximum bandwidth

The rendering reward formula of the Paraverse network uses the GPU performance, video memory, memory, vCPU performance, and maximum bandwidth of the graphics card as independent variables after in-depth research and professional consideration. Taking the graphics card configuration as the main consideration can more accurately evaluate the contribution of miners in rendering tasks and reward them accordingly. In the Web3.0 rendering business, GPU performance and video memory play an important role, so we use them as key factors for token rewards. This professional setting ensures that our reward mechanism is consistent with industry practices and user needs. For the selection and setting of the independent variables of the rendering reward formula, we will focus on the computing performance of the graphics card rather than the price of the graphics card in the market. Because in rendering tasks, computing performance has a direct impact on the execution speed and efficiency of the task. Although the price reflects the cost-effectiveness of the graphics card, it is not a factor that directly determines the contribution of the rendering task. Therefore, in the rendering reward formula, we will pay more attention to the relevant performance indicators of the graphics card configuration, rather than taking the price as a consideration. Such a reward setting can better meet the needs of the decentralized rendering business and provide miners with fair and reasonable token rewards. High-performance graphics cards usually have higher floating-point operation capabilities and can provide higher computing performance, thereby showing higher contributions in rendering tasks. At the same time, we will continue to pay attention to the market dynamics and technological development of graphics cards, ensure that the reward mechanism keeps pace with technological innovation, and evaluate and adjust our reward formula coefficient settings to provide miners with the best incentives and returns. Such a weight setting can not only ensure the fairness and rationality of the rendering reward mechanism but also motivate miners to continuously improve the performance of graphics card configurations and promote the development and progress of the entire network.

Rendering Revenue Formula Independent Variable Weight Setting

In the rendering reward formula, different hardware parameter configurations, such as GPU performance, video memory, memory, vCPU performance, and maximum bandwidt,h will have different degrees of impact on the overall rendering efficiency and quality, so quantifying the weights of these five dependent variables is a crucial step.

1. GPU performance (TFLOPS SP / 30T INT) and rendering capability

• Correlation: High

• Explanation: GPU performance directly determines the execution efficiency of rendering tasks, especially in graphics-intensive tasks. TFLOPS SP (single-precision floating-point operations per second) and INT computing power (integer computing power) are indicators of GPU computing power. SP floating-point operations are crucial for graphics tasks such as 3D rendering and ray tracing. The higher the TFLOPS value, the more computing tasks the GPU can perform per second and the faster the rendering speed. Therefore, the performance of the GPU is highly positively correlated with the rendering speed.

1. Video Memory (GB) and Rendering Capability

• Correlation: Medium to High

• Explanation: Video memory capacity determines the amount of data that the GPU can load and process during the rendering process. A large amount of video memory is required during the rendering process to store data such as models, textures, and lighting caches. Insufficient video memory can cause task overflows, forcing the GPU to rely on slower system memory, which significantly reduces rendering performance. In particular, when rendering large 3D scenes or ultra-high-definition images, insufficient video memory can seriously affect performance. Therefore, video memory capacity is significantly positively correlated with rendering capabilities, especially when dealing with complex scenes.

1. Memory (GB) and Rendering Capability

• Correlation: Medium to High

• Explanation: During the rendering process, a large amount of geometric data, textures, materials, and lighting information need to be stored and quickly accessed. If there is insufficient memory, the system will rely on the disk for data exchange (i.e., using virtual memory), which will greatly reduce rendering performance. Therefore, the larger the memory, the more complex scenes the rendering task can handle, and the better the overall performance, especially when dealing with ultra-high-resolution scenes or multi-layered materials, the role of memory is significantly enhanced.

1. vCPU performance (cores) and rendering capabilities

• Correlation: Medium to high

• Explanation: The more cores a vCPU has, the stronger its parallel processing capabilities are in theory. This is crucial for CPU rendering (such as using Blender's CPU rendering mode), as more cores can handle more rendering tasks simultaneously, thereby reducing rendering time. Although GPU rendering has gradually replaced CPU rendering as the mainstream, vCPU performance is still crucial in some tasks (such as scene parsing, data loading, task distribution, etc.).

1. Maximum bandwidth (Mbps) and rendering capabilities

• Correlation: Medium

• Explanation: Bandwidth mainly affects the speed of data transmission, especially when the rendering task needs to download a large amount of scene data from remote resources (such as cloud storage or other network nodes). The larger the bandwidth, the faster the data transmission and the faster the data loading speed during the rendering process. Although bandwidth does not directly affect the rendering calculation process, it will play a greater role in multi-node distributed rendering and when online collaboration is required. Therefore, there is a certain positive correlation between bandwidth and rendering capabilities, especially when the rendering task depends on high-frequency data exchange.

According to the analysis, GPU performance and video memory have the greatest impact on rendering capabilities, while the maximum bandwidth has a relatively small impact, and the others are in the middle. Therefore, the coefficients can be set based on their relevance. The following is the weight coefficient allocation for each dependent variable:

Dependent variable	Weight coefficient	Reason
GPU performance	0.4	GPU performance is the most important determining factor for rendering, directly affecting rendering speed and efficiency.
Video memory	0.25	Video memory capacity determines the upper limit of complex scenes and high-resolution rendering, and has a greater impact on rendering.
Memory	0.15	A large amount of memory can support the processing of more data, especially when multiple tasks are parallel, the impact is medium.
vCPU performance	0.15	vCPU has a medium impact on some non-graphics processing rendering tasks (such as scene loading).
Maximum bandwidth	0.05	Bandwidth has little impact on single-machine rendering, but it has a certain effect in distributed rendering and network environments.

Example of calculating the number of rendering task rewards issued

For example, 1 PAR token is generated within one hour as the rendering income of the miner. At this time, there are three orders for running rendering tasks, namely task 1 (running for 30 minutes), task 2 (running for 45 minutes) and task 3 (running for one hour). The mining machine configuration information corresponding to the three tasks is as follows:

Mining machine configuration information	GPU	Video Memory	Memory	CPU	Bandwidth
Mining machine accepting task 1	GPU performance 16 TF SP / 30T INT and above	24G and above	44GB and above	12 cores and above vCPU performance	Up to 10Mbps
Mining machine accepting task 2	GPU performance 8.1 TF SP / 30T INT and above	12G and above	32GB and above	10 cores and above vCPU performance	Up to 8Mbps
Mining machine accepting task 3	GPU performance 2 TF SP / 30T INT and above	4G and above	8GB and above	4 cores and above vCPU performance	Up to 6Mbps

The calculation method for rendering rewards earned by miners executing rendering tasks within a given time unit is as follows:

1. Assigning Weight Coefficients for Each Factor:

   1. GPU Performance: 0.4

   2. VRAM: 0.25

   3. RAM: 0.15

   4. vCPU Performance: 0.15

   5. Maximum Bandwidth: 0.05

2. Calculating the Rendering Power Score for Each Miner based on GPU performance, VRAM, RAM, vCPU performance, maximum bandwidth, task runtime, and weight coefficients:

   1. Rendering power score for Task 1 = (16×0.4 + 24×0.25 + 44×0.15 + 12×0.15 + 10×0.05) × 30/60 = 10.65

   2. Rendering power score for Task 2 = (8.1×0.4 + 12×0.25 + 32×0.15 + 10×0.15 + 8×0.05) × 45/60 = 9.705

   3. Rendering power score for Task 3 = (2×0.4 + 4×0.25 + 8×0.15 + 4×0.15 + 6×0.05) × 60/60 = 3.9

3. Normalizing the Rendering Power Scores to bring all values within the range of 0 to 1:

   1. Normalized score for Task 1 = 10.65 / 10.65 ≈ 1

   2. Normalized score for Task 2 = 9.705 / 10.65 ≈ 0.9113

   3. Normalized score for Task 3 = 3.9 / 10.65 ≈ 0.3662

4. Applying Reward Proportions to the Total Rewards in the Given Time Unit by scaling the reward ratios. Assuming the scaling factor is x, solving for x in the equation:

x+0.9113x+0.3662x=1x + 0.9113x + 0.3662x = 1x+0.9113x+0.3662x=1Solving for x, we get x ≈ 0.4391.Therefore, the final reward allocations are:

• Task 1 reward = 1 × 0.4391 ≈ 0.4391 tokens

• Task 2 reward = 0.9113 × 0.4391 ≈ 0.4002 tokens

• Task 3 reward = 0.3662 × 0.4391 ≈ 0.1608 tokens

	Rendering Power Score	Runtime	Rendering Reward
task 1	10.65	30min	0.4391
task 2	9.705	45min	0.4002
task 3	3.9	60min	0.1608