Graphics Processing Units

Abstract

Graphics Processing Units (GPUs) are coprocessors that traditionally perform the rendering of 2-dimensional and 3-dimensional graphics information for display on a screen. In particular computer games request more and more realistic real-time rendering of graphics data and so GPUs became more and more powerful highly parallel specialist computing units. It did not take long until programmers realized that this computational power can also be used for tasks other than computer graphics. For example already in 1990 Lengyel, Reichert, Donald, and Greenberg used GPUs for real-time robot motion planning [43]. In 2003 Harris introduced the term general-purpose computations on GPUs (GPGPU) [28] for such non-graphics applications running on GPUs. At that time programming general-purpose computations on GPUs meant expressing all algorithms in terms of operations on graphics data, pixels and vectors. This was feasible for speed-critical small programs and for algorithms that operate on vectors of floating-point values in a similar way as graphics data is typically processed in the rendering pipeline.The programming paradigm shifted when the two main GPU manufacturers, NVIDIA and AMD, changed the hardware architecture from a dedicated graphics-rendering pipeline to a multi-core computing platform, implemented shader algorithms of the rendering pipeline in software running on these cores, and explicitly supported general-purpose computations on GPUs by offering programming languages and software-development toolchains.This chapter first gives an introduction to the architectures of these modern GPUs and the tools and languages to program them. Then it highlights several applications of GPUs related to information security with a focus on applications in cryptography and cryptanalysis.

Investigating SRAM PUFs in large CPUs and GPUs

“…Contrary to the CPU, which provides security features such as memory protection and therefore has clear reasons to prevent direct access to the SRAM, GPUs often expose their SRAM directly to the programmer, and also do not have the same reasons to clear the SRAM after reset. GPU memory and registers leak sensitive data between processes, as observed in [21] and later in [8]; the absence of memory zeroing between processes, where sensitive data may be handled, suggests that zeroing to prevent reading uninitialized memory is also absent.…”