Encryption

Why is Klepsydra SDK interesting for algorithm encryption? 

Klepsydra SDK is beneficial in two different scenarios.  

• Large volumes of data to be processed, for example, for high resolution images, Lidar data or IMU data. When sensors produce data at a very high rate and a large volume, Klepsydra can be used to process them in a more efficient way in real time. 
• Processor intensive algorithms, like convolutions in artificial intelligence, kalman filters, matrix multiplication etc. This is the case of encryption algorithms. 

 In this article, a benchmark application using the Rijndael Algorithm is presented to demonstrate the power of Klepsydra SDK for encryption. 

Why this implementation and what are the benefits? 

• With the separation in event loops, there is control over the CPU and, therefore, the number of cores dedicated to the encryption can be configured.  
• The consumption of the CPU decreases, because pipelines tend to reduce significantly the CPU consumption. 

• The throughput increases as well since the parallelization in a pipeline optimises the use of the available cores. 

How is encryption optimised?

Klepsydra SDK has a tool, the streaming API, that enables acceleration of processor intensive algorithms. There are the two main approaches that can be used to optimise algorithms: pipelining and parallelization. Both methods are explained below. 

Pipelining

First, the event loop pipelining: the parallelization is done in a pipeline, that looks like an assembly line, where the encryption algorithm is divided in smaller parts and each of them is executed in a different event loop. 

 In the case of Rijndael Algorithm, this is quite a straightforward because Rijndael encrypts block by block. First, the first block of 16 bytes, then the next block etc. A very straightforward implementation with Klepsydra, is where the first 16-byte block is processed by the 1st event loop, the second 16-byte block by the second event loop and so on. 

Why this implementation and what are the benefits? 

• With the separation in event loops, there is control over the CPU and, therefore, the number of cores dedicated to the encryption can be configured.  
• The consumption of the CPU decreases, because pipelines tend to reduce significantly the CPU consumption. 

• The throughput increases as well since the parallelization in a pipeline optimises the use of the available cores. 

Parallelization

There is, however, another approach to optimization, which is using Klepsydra data multiplexer parallelization. 

 The SDK has two APIs: the event-loop, above presented, and the data multiplexer, which consists of processing the same piece of data in parallel by multiple consumers.  

 In this approach, the streaming passes the full string to the data multiplexer and each consumer will process one block, so the first consumer processes the first 16-byte block, the second consumer the second 16-byte block and so on. 

 At the end there is a requirement to put it all together, a final synchronization, that is done also by the event-loop automatically.