Achieve compression ratios over 3000:1 for any file type with lossless data compression, reduce storage and transfer costs by up to 99%, and significantly enhance your system's processing speeds and performance.
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The SPIRAL-BOX Workstation revolutionizes data management by achieving a compression ratio exceeding 3000:1, significantly surpassing the conventional 4:1 ratio. This enables it to compress 1 TB files down to less than 0.33 GB using lossless compression, substantially reducing storage and transfer costs. The system operates solely via external disks or connections, without storing any data internally, enhancing data security. With strong encryption and no internet connectivity, it provides a secure and efficient way to manage data.
Performance capabilities
| Encode (GB/s) | Decode (GB/s) | |
|---|---|---|
| CPU 8cores | 8.84 | 1.66 |
| RTX 4090 | 439.36 | 42.66 |
| L40S | 4032.26 | 391.48 |
SPIRAL-BOX's lossless compression ensures that all original data can be perfectly reconstructed upon decompression, maintaining the highest quality for critical applications.
With SPIRAL-BOX, data can be stored using significantly less space without sacrificing quality, making it an ideal solution for industries facing exponential data growth.
Lossless compression provided by SPIRAL-BOX ensures that data integrity is uncompromised, which is essential for compliance with regulatory requirements and for maintaining high security standards.
By compressing data, SPIRAL-BOX significantly reduces the need for physical storage and data transfer resources, cutting down expenses associated with data management and operational overhead, contributing to substantial cost savings across the organization.
SPIRAL-BOX enhances the speed of data transfer, crucial for organizations that rely on the quick availability of large datasets, such as video streaming services and online content providers.
By reducing the data payload size, SPIRAL-BOX minimizes the bandwidth required for data transfers, facilitating more efficient use of network resources and reducing bottlenecks in network-intensive environments.
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Our method works independently of statistical patterns or dictionaries, allowing additional compression even after algorithms like Huffman, ANS, or LZW have been applied.
Our approach excels in compressing random or unstructured data, where traditional algorithms struggle. We can efficiently compress data larger than 9 bytes without relying on patterns, offering an advantage for industries handling complex datasets.
We use a unique mathematical encoding that doesn’t depend on patterns or probabilities, enabling us to compress random data and even surpass the theoretical limitations imposed by Shannon’s limit, which constrains traditional compression methods.
Our method can further reduce file sizes even after Huffman or ANS compression. Since we don’t rely on patterns or dictionaries, we provide an extra layer of compression beyond what these algorithms can achieve, overcoming the restrictions of entropy-based methods.