Accelerating private-key cryptography via multithreading on symmetric multiprocessors
Bibliographic record
Abstract
Achieving high performance in cryptographic processing is important due to the increasing connectivity among today's computers. Despite steady improvements in microprocessor and system performance, private-key cipher implementations continue to be slow. Irrespective of the cipher used, the main reason for the low performance is lack of parallelism, which fundamentally comes from encryption modes such as the Cipher Block Chaining (CBC) mode. In CBC, each plaintext block is XOR'ed with the previous ciphertext block and then encrypted, essentially inducing a tight recurrence through the ciphertext blocks. To deliver high performance while maintaining high level of security assurance in real systems, the cryptography community has proposed Interleaved Cipher Block Chaining (ICBC) mode. In four-way interleaved chaining, the first, fifth, and every fourth block thereafter are encrypted in CBC mode; the second, sixth, and every fourth block thereafter are encrypted as another stream, and so on. Thus, interleaved chaining loosens the recurrence imposed by CBC, enabling the multiple encryption streams to be overlapped. The number of interleaved chains can be chosen to balance performance and adequate chaining to get good data diffusion. While ICBC was originally proposed to improve hardware encryption rates by employing multiple encryption chips in parallel, this is the first paper to evaluate ICBC via multithreading commonly-used ciphers on a symmetric multiprocessor (SMP). ICBC allows exploiting the full processing power of SMPs, which spend many cycles in cryptographic processing as medium-scale servers today, and will do so as chip-multiprocessor clients in the future. Using the Wisconsin Wind Tunnel II, we show that our multithreaded ciphers achieve encryption rates of 92 Mbytes/s on a 16-processor SMP at 1 GHz, reaching a factor of almost 10 improvement oiler a uniprocessor, which achieves 9 Mbytes/s.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".