Sammanfattning Bakgrund - Dagens processorer börjar närma sig gränsen för hur höga klockfrekvenser de kan köras i. Detta har lett till att processorer har fått fler kärnor för att kunna exekvera flera processer parallellt med flertrådade applikationer. Det finns dock ofta en stor mängd oanvänd beräkningskraft under långa perioder då datorn är igång som ligger i grafikprocessorn, GPU. Då en GPU kan köra tusentals många fler trådar på samma gång än en CPU har ramverk för att göra mer generella beräkningar på GPU utvecklats, dessa kallas för GPGPU-ramverk. Då varje kärna på en GPU inte är lika stark som på en CPU ligger vinsten i att använda algoritmer som går bra att parallellisera. En sådan algoritm är krypteringsalgoritmen AES som är en av de säkraste och vanligaste krypteringsalgoritmerna som används idag. Syfte – Med hjälp av GPU-accelerering kan man kryptera med AES snabbare än med en traditionell CPU-lösning. För att göra GPU-accelereringen så effektiv som möjligt undersöker detta examensarbete vilken AES-metod samt vilket GPGPU-ramverk man bör välja. Metod – För att undersöka vilken/vilka AES-metoder samt vilka GPGPU-ramverk som var lämpliga att använda för denna undersökning gjordes två litteraturstudier. Utifrån data som litteraturstudierna gav genomfördes experiment för att jämföra de valda GPGPU-ramverken med den valda AES-metoden som ansågs vara mest lämpliga. Resultat – Från litteraturstudierna kom det fram att OpenCL och CUDA blir de rekommenderade GPGPU-ramverken och att CTR blir den rekommenderade AES-metoden för AES-kryptering med GPGPU-programmering. Utifrån experimenten som genomförts kunde det konstateras att CUDA är ett effektivare GPGPU-ramverk än OpenCL för AES-CTR på det testade grafikkortet, GTX 560. Implikationer – CUDA är snabbare vid större filer för att OpenCL begränsas mer av dataöverföringshastigheten än CUDA på ett GTX 560. Begränsningar – Experimenten genomfördes endast på ett grafikkort från Nvidia. Eftersom Nvidia inte har något intresse i att optimera för andra GPGPU-ramverk så kunde inte testresultaten från OpenCL verifieras med externa verktyg. Detta p.g.a. att Nvidias verktyg inte längre stödjer debugging eller profiling för OpenCL. Nyckelord – Processorer, GPGPU, AES, CTR, OpenCL, CUDA, GPGPU-ramverk / Abstract Background - Processors today are approaching the limit for how high clockfrequences they can run. This has led to that instead of trying to make them run faster they are instead made with multiple cores so they can utilize parallelization by running several threads in parallel. However aside from the CPU there is still the graphics card which has a large amount of unused computing power for long durations of time while the computer is active. While a GPU might not have as quick processors it instead has several thousands of them at the same time than a CPU which have led to the development of GPGPU-frameworks to use that potential parallelization. The profit in this lies in using algorithms and code functions that got high potential parallelization, one of which is the AES encryption algorithm. AES is one of the most widely used encryption algorithms today and also considered to be one of the most secure. Purpose – By using GPGPU-acceleration the encryption speed of AES is higher than by using a traditional CPU approach. To make the GPU-acceleration as effective as possible this study looks into which AES-method and which GPGPU-framework that should be chosen during development. Method – This study makes two literature studies to determine which AES-methods and which GPGPU-frameworks that are viable for GPU-acceleration of AES. Afterwards this study conducts experiments to determine which of these GPGPU-frameworks are the most effective. Findings – The conclusion drawn from the literature study is that the CTR-method among the AES-methods is preferable due to its parallelization potential and high security measures. Among the current GPGPU-frameworks only two frameworks satisfies the criteria determined from the literature study and those are CUDA and OpenCL. From the experiment the conclusion is thereafter drawn that of the two GPGPU-frameworks CUDA is more effective due to the bandwidth limits that OpenCL have compared to CUDA. This conclusion is valid on at least the tested graphics card, GTX 560. Implications – CUDA is faster at larger file sizes than OpenCL due to limited data transfer speed in OpenCL on a GTX 560. Limitations – The experiments were only conducted on one graphics card from Nvidia due to hardware constraints in that CUDA can only be run on Nvidia hardware. Due to this hardware constraint and Nvidia’s lack of support in their tools for debugging and profiling of OpenCL the results from the testing of OpenCL couldn’t be verified using external tools. Keywords – Processor, GPGPU, AES, CTR, OpenCL, CUDA, GPGPU-framework

Purpose – The purpose of this study is partly to illustrate how the AES encryption methods ECB and CTR affect the computational speed when using the GPGPU framework Cuda, but also to clarify the advantages and disadvantages between the different AES encryption modes. Method – A preliminary study was conducted to obtain empirical data on the AES encryption modes ECB and CTR. Data from the study has been analyzed and compared to determine the various aspects of the AES encryption modes and to create a basis for determining the advantages and disadvantages between them. The preliminary study has been carried out systematically by finding scientific works by searching databases within the subject. An experiment has been used as a method to be able to extract execution time data for the GPGPU framework Cuda when processing the AES encryption modes. Experiment were chosen as a method to gain control over the variables included in the study and to see how these variables change when they are consciously influenced. Findings – The findings of the preliminary study show that CTR is more secure than the ECB, but also considerably more complex, which can lead to integrity risks when implementation is done incorrectly. In the experiment, computational speeds are produced when the CPU memory sends to the GPU memory, the encryption on the GPU and how long it takes for the GPU memory to send to the CPU memory. This is done for both CTR and ECB in encryption and decryption. The result of the analysis shows that the ECB is faster than CTR in encryption and decryption. The calculation speed is higher with the ECB compared to the CTR. Implications – The experiment shows that CTR is slower than the ECB. But the most amount of time spent in encryption for both modes are the transfers between the CPU memory and the GPU memory. Limitations – The file sizes of the files tested only goes up to about 1 gigabyte which gave small computation times.