Abstract Nowadays we finally see the strong convergence of high-performance computing (HPC) and embedded systems, in order to increase system-level energy efficiency and runtime performance. And at the same time, we observe a growing adoption of cloud computing, a pay-per-use service providing access to a shared pool of hardware and software resources. One industry that is currently profiting from both trends is finance. In this regard, financial risk management is nowadays a key part of the daily operations in the financial industry. Its importance became evident when failure to properly control and manage risk exposure contributed to the latest financial collapses, including the meltdown and bankruptcy of large financial institutions worldwide. As a result, stricter regulations have been set into place, enforcing for example the regular monitoring of risk measures. And from the technology standpoint, two trends are visible nowadays in the financial industry: the growing transition into cloud computing services, and the widespread adoption of electronic trading systems. Under these circumstances, there is a real need in this field for computing systems with low latency, high throughput, and high energy efficiency. This is an excellent case where heterogeneous energy-efficient HPC systems can be deployed. They not only exploit the capabilities of central processing units (CPUs), but also include one or several types of hardware accelerators, such as many integrated core (MIC) architectures, graphics processor units (GPUs), and field programmable gate arrays (FPGAs). To handle this heterogeneity, the Open Computing Language (OpenCL) framework is widely used nowadays to generate portable parallel code. And although this heterogeneity makes these systems more flexible, it also introduces a series of challenges to be addressed in order to efficiently implement any given application. In this context, the goal of my research has been to efficiently implement relevant financial risk management applications on heterogeneous energy-efficient HPC systems. The novel contributions presented in this thesis are bundled into four main topics: 1. a massive acceleration of the risk measurement of complex financial portfolios under compute-intensive nested Monte Carlo (MC)-based simulations, making this approach feasible for intraday operation. This also includes the real-time continuous risk monitoring of dynamic portfolios, and a cloud-based implementation test case. 2. the efficient implementation of theMC-based Longstaff-Schwartz algorithm on FPGAbased systems, including tailor-made algorithmic optimizations, to price multidimensional American-style options. Options are derivative contracts widely employed for risk hedging, and they are also extensively traded in the financial market. 3. the efficient decoupling of parallel OpenCL work-items on FPGAs, to maximize the performance of parallelized algorithms that contain data-dependent branches, where these work-items diverge. Test case: a nested rejection-based gamma-distributed random number generator (RNG), typically used in CreditRisk+ simulations. 4. the evaluation, in an interdisciplinary research environment, of the typical trade-off between programming productivity and implementation efficiency. Test case base combination: Python (an interpreter language) for productivity, the PyOpenCL package for efficiency, all contained in a Jupyter Notebook (a web-based application). These novel contributions are the outcome of a successful interdisciplinary research cooperation between financial mathematics and computer engineering, in the scope of the Research Training Group RTG 1932 “Stochastic Models for Innovation in the Engineering Sciences”, funded by the Deutsche Forschungsgemeinschaft (DFG). In this regard, we have developed a remarkably strong interaction between both fields, where the optimizations and characteristics of each side have influenced the other one, in a very profitable way.weiterlesen