Comparing various hardware configurations needed for different data sizes, from personal laptops to mainframes.
The dataset titled "Data_Hardware.csv" provides detailed information about various computer systems categorized by their hardware specifications. It includes columns for data size (measured in MB or GB), CPU cores, RAM size in GB, storage capacity in TB, network speed in Gbps, and examples of CPUs and GPUs for each configuration. Additionally, it lists the type of computer (e.g., personal laptop, desktop PC, workstation) and the corresponding file size in MB. The table captures a range of systems from basic personal laptops to high-performance mainframes, illustrating the diversity in hardware capabilities based on different use cases.
One of the key columns added to the table is "Total Time (minutes)", which provides a combined and adjusted measure of processing time using both CPU and GPU. This column offers a realistic view of the potential performance improvements when utilizing GPU acceleration alongside CPU processing. For example, a personal laptop processing 100 MB of data takes approximately 4.35 minutes, highlighting the significant reduction in time due to GPU acceleration compared to relying solely on the CPU. This demonstrates the efficiency gains possible with modern hardware configurations, even for lower-end systems.
The mid-range systems such as desktop PCs and workstations show notable improvements in processing times when GPUs are utilized. A desktop PC handling 500 MB of data takes 8.70 minutes, while a workstation processing 1 GB takes 21.75 minutes. These reductions are significant when compared to CPU-only times, emphasizing the importance of GPUs in accelerating computational tasks. The GPUs listed, such as the NVIDIA GeForce GTX 1660 and NVIDIA Quadro RTX 4000, are well-suited for mid-range and professional applications, providing a balance of performance and cost.
At the high end of the spectrum, high-performance PCs and mainframes exhibit drastic reductions in processing times with the inclusion of GPUs. A high-performance PC processing 10 GB of data takes 43.50 minutes, and a server handling 100 GB takes 87.00 minutes. Mainframes, which are designed for massive data processing tasks, also show significant improvements. For instance, a mainframe processing 1,000 GB takes 43.50 minutes, and for 100,000 GB, it takes 174.00 minutes. The use of powerful GPUs such as the NVIDIA A100 and AMD Radeon Instinct MI100 in these systems highlights their capability to handle extensive parallel processing tasks efficiently, making them ideal for large-scale enterprise and scientific computing environments.
| Data Size (MB/GB) | CPU (Cores) | RAM (GB) | Storage (TB) | Network (Gbps) | CPU Examples | Computer Type | File Size (MB) | GPU Examples | Total Time (minutes) |
|---|---|---|---|---|---|---|---|---|---|
| 100 MB | 1 | 1 | 0.01 | 0.1 | Intel Core i5-1135G7, AMD Ryzen 5 4500U | Personal Laptop | 100 | NVIDIA GeForce MX350, AMD Radeon RX 540 | 4.35 |
| 500 MB | 2 | 2 | 0.05 | 0.5 | Intel Core i5-10400, AMD Ryzen 5 3600 | Desktop PC | 500 | NVIDIA GeForce GTX 1660, AMD Radeon RX 580 | 8.70 |
| 1 GB | 2 | 4 | 0.10 | 1.0 | Intel Xeon W-10885M, AMD Ryzen Threadripper 3960X | Workstation | 1024 | NVIDIA Quadro RTX 4000, AMD Radeon Pro WX 7100 | 21.75 |
| 10 GB | 4 | 16 | 0.50 | 1.0 | Intel Core i9-11900K, AMD Ryzen 9 5900X | High-Performance PC | 10240 | NVIDIA GeForce RTX 3080, AMD Radeon RX 6900 XT | 43.50 |
| 100 GB | 8 | 32 | 1.00 | 2.0 | Intel Xeon E-2288G, AMD EPYC 7742 | Server | 102400 | NVIDIA Tesla V100, AMD Radeon Instinct MI60 | 87.00 |
| 1000 GB | 16 | 64 | 10.00 | 10.0 | IBM z15 T01, IBM z14 ZR1 | Mainframe | 1024000 | NVIDIA A100, AMD Radeon Instinct MI100 | 43.50 |
| 10000 GB | 32 | 128 | 50.00 | 40.0 | IBM z15 T01, IBM z14 ZR1 | Mainframe | 10240000 | NVIDIA A100, AMD Radeon Instinct MI100 | 87.00 |
| 100000 GB | 64 | 256 | 200.00 | 100.0 | IBM z15 T01, IBM z14 ZR1 | Mainframe | 102400000 | NVIDIA A100, AMD Radeon Instinct MI100 | 174.00 |
This table and calculations could be expanded by processing larger or smaller amounts of data using the same hardware.
| Data Size (MB/GB) | CPU (Cores) | RAM (GB) | Storage (TB) | Network (Gbps) | CPU Examples | Computer Type | File Size (MB) | GPU Examples | Total Time (minutes) |
|---|---|---|---|---|---|---|---|---|---|
| 100 GB | 4 | 16 | 0.512 | 1.0 | AMD Ryzen 5 7520U | Mid-range Laptop | 102400 | Radeon Graphics | 110.5 |
The table presents data on the specifications and performance of a mid-range laptop processing a 100 GB dataset. The laptop is equipped with a quad-core AMD Ryzen 5 7520U CPU, 16 GB of RAM, and 512 GB of storage. It has a network bandwidth of 1 Gbps and utilizes Radeon Graphics for GPU tasks. The file size for processing is noted to be 102400 MB. The total time taken for the complete processing of this dataset is 110.5 minutes. This setup reflects a balanced configuration suitable for moderate data processing tasks, typical of mid-range laptops.
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