Ray tracing is a computationally complex global illumination algorithm that is used for producing realistic images. In addition to parallel implementations on commodity PC clusters, recently, Graphics Processing Units (GPU) have also been used to accelerate ray tracing. In this thesis, ray tracing is accelerated on a GPU cluster where the viewing plane is divided into unit tiles. Slave processes work on these tiles in a task parallel manner which are dynamically assigned to them. To decrease the number of ray-triangle intersection tests, Bounding Volume Hierarchies (BVH) are used. It is shown that almost linear speedup can be achieved. On the other hand, it is observed that API and network overheads are obstacles for scalability.

The advances in computing and communication technologies and software tools have resulted in an explosive growth in networked applications and information services that cover all aspects of our life. These services and applications are inherently complex, dynamic and heterogeneous. In a similar way, the underlying information infrastructure, e.g. the Internet, is large, complex, heterogeneous and dynamic, globally aggregating large numbers of independent computing and communication resources. The combination of the two results in application development and management complexities that break current computing paradigms, which are based on static behaviors. As a result, applications, programming environments and information infrastructures are rapidly becoming fragile, unmanageable and insecure. This has led researchers to consider alternative programming paradigms and management techniques that are based on strategies used by biological systems. Autonomic programming paradigm is inspired by the human autonomic nervous system that handles complexity, uncertainties and abnormality. The overarching goal of the autonomic programming paradigm is to help building systems and applications capable of self-management. Firstly, we investigated the large-scale scientific computing applications which generally experience different execution phases at run time and each phase has different computational, communication and storage requirements as well as different physical characteristics. In this dissertation, we present Physics Aware Optimization (PAO) paradigm that enables programmers to identify the appropriate solution methods to exploit the heterogeneity and the dynamism of the application execution states. We implement a Physics Aware Optimization Manager to exploit the PAO paradigm. On the other hand we present a self configuration paradigm based on the principles of autonomic computing that can handle efficiently complexity, dynamism and uncertainty in configuring server and networked systems and their applications. Our approach is based on making any resource/application to operate as an Autonomic Component (that means it can be self-managed component) by using our autonomic programming paradigm. Our POA technique for medical application yielded about 3X improvement of performance with 98.3% simulation accuracy compared to traditional techniques for performance optimization. Also, our Self-configuration management for power and performance management in GPU cluster demonstrated 53.7% power savings for CUDAworkload while maintaining the cluster performance within given acceptable thresholds.

In the last decade, computational power, memory bandwidth and programmability capabilities of graphics processing units (GPU) have rapidly evolved. Therefore, many researches have been performed to use GPUs in advanced graphics rendering. Because of its high degree of parallelism, ray tracing has been one of the rst algorithms studied on GPUs. However, the rendering of large scenes with ray tracing can easily exceed the GPU&#039 / s memory capacity. The algorithm proposed in this work uses a data parallel approach where the scene is partitioned and assigned to CPU-GPU couples in a cluster to overcome this problem. Our algorithm focuses on ray casting which is a special case of ray tracing mainly used in visualization of volumetric data. CPUs are pretty ecient in ow control and branching while GPUs are very fast performing intense oating point operations. Using these facts, the GPUs in the cluster are assigned the task of performing ray casting while the CPUs are responsible for traversing the rays. In the end, we were able to visualize large scenes successfully by utilizing CPU-GPU couples eectively and observed that the performance is highly dependent on the viewing angle as a result of load imbalance.