Abstract. Graphics Processing Units (GPUs) present large potential performance gains within stream processing applications over the standard CPU. These performance gains are best realised when high computational intensity is required across large amounts of mostly independent input elements. The GPU's success in general purpose stream processing has been demonstrated in many diverse fields, though attempts to port cryptographic algorithms to the GPU have thus far met little success. In recent years, GPU architectures have continued to develop a more flexible and uniform programming environment. These developments have overcome a lot of previously encountered restrictions in cipher implementations. We present novel approaches for the implementation of the AES block cipher encryption algorithm on these GPUs. This work also serves as a precursor for future cipher implementations on the most advanced GPU architecture, the recently released Nvidia G80, which now includes integer support and a simplified programming interface.

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Abstract. Graphics processing units (GPU) are increasingly being used for general purpose computing. We present implementations of large integer modular exponentiation, the core of public-key cryptosystems such as RSA, on a DirectX 10 compliant GPU. DirectX 10 compliant graphics processors are the latest generation of GPU architecture, which provide increased programming flexibility and support for integer operations. We present high performance modular exponentiation implementations based on integers represented in both standard radix form and residue number system form. We show how a GPU implementation of a 1024-bit RSA decrypt primitive can outperform a comparable CPU implementation by up to 4 times and also improve the performance of previous GPU implementations by decreasing latency by up to 7 times and doubling throughput. We present how an adaptive approach to modular exponentiation involving implementations based on both a radix and a residue number system gives the best all-around performance on the GPU both in terms of latency and throughput. We also highlight the usage criteria necessary to allow the GPU to reach peak performance on public key cryptographic operations.

A recent study [7] has shown that many computing students are not able to develop straightforward programs after the introductory programming sequence. Normal student assessment should have highlighted this problem, it did not, therefore normal assessment of programming ability does not work.We examine why current assessment methods (written exams and programming assignments) are faulty. We investigate another method of assessment (the lab exam) and show that this form of assessment is more accurate.We explain why accurate assessment is essential in order to encourage students to develop programming ability.

Virtual machines (VMs) are a popular target for language implementers. A longrunning question in the design of virtual machines has been whether stack or register architectures can be implemented more efficiently with an interpreter. Many designers favour stack architectures since the location of operands is implicit in the stack pointer. In contrast, the operands of register machine instructions must be specified explicitly. In this paper, we present a working system for translating stackbased Java virtual machine (JVM) code to a simple register code. We describe the translation process, the complicated parts of the JVM which make translation more difficult, and the optimisations needed to eliminate copy instructions. Experimental results show that a register format reduced the number of executed instructions by 34.88%, while increasing the number of bytecode loads by an average of 44.81%. Overall, this corresponds to an increase of 2.32 loads for each dispatch removed. We believe that the high cost of dispatches makes register machines attractive even at the cost of increased loads.