The interest of using GPU:s as general processing units for heavy computations (GPGPU) has increased in the last couple of years. Manufacturers such as Nvidia and AMD make GPU:s powerful enough to outrun CPU:s in one order of magnitude, for suitable algorithms. For embedded systems, GPU:s are not as popular yet. The embedded GPU:s available on the market have often not been able to justify hardware changes from the current systems (CPU:s and FPGA:s) to systems using embedded GPU:s. They have been too hard to get, too energy consuming and not suitable for some algorithms. At SICK IVP, advanced computer vision algorithms run on FPGA:s. This master thesis optimizes two such algorithms for embedded GPU:s and evaluates the result. It also evaluates the status of the embedded GPU:s on the market today. The results indicates that embedded GPU:s perform well enough to run the evaluatedd algorithms as fast as needed. The implementations are also easy to understand compared to implementations for FPGA:s which are competing hardware.

Multicore CPUs (Central Processing Units) and GPUs (Graphics Processing Units) are omnipresent in today’s market-leading smartphones and tablets. With CPUs and GPUs getting more complex, maximizing hardware utilization is becoming problematic. The challenges faced in GPGPU (General Purpose computing using GPU) computing on embedded platforms are different from their desktop counterparts due to their memory and computational limitations. This thesis evaluates the performance and energy efficiency achieved by offloading Java applications to an embedded GPU. The existing solutions in literature address various techniques and benefits of offloading Java on desktop or server grade GPUs and not on embedded GPUs. Our research is focussed on providing a framework for accelerating Java programs on embedded GPUs. Our experiments were conducted on a Freescale i.MX6Q SabreLite board which encompasses a quad-core ARM Cortex A9 CPU and a Vivante GC 2000 GPU that supports the OpenCL 1.1 Embedded Profile. We successfully accelerated Java code and reduced energy consumption by employing two approaches, namely JNI-OpenCL, and JOCL, which is a popular Java-binding for OpenCL. These approaches can be easily implemented on other platforms by embedded Java programmers to exploit the computational power of GPUs. Our results show up to an 8 times increase in performance efficiency and 3 times decrease in energy consumption compared to the embedded CPU-only execution of Java program. To the best of our knowledge, this is the first work done on accelerating Java on an embedded GPU.

Multicore CPUs (Central Processing Units) and GPUs (Graphics Processing Units) are omnipresent in today’s market-leading smartphones and tablets. With CPUs and GPUs getting more complex, maximizing hardware utilization is becoming problematic. The challenges faced in GPGPU (General Purpose computing using GPU) computing on embedded platforms are different from their desktop counterparts due to their memory and computational limitations. This thesis evaluates the performance and energy efficiency achieved by offloading Java applications to an embedded GPU. The existing solutions in literature address various techniques and benefits of offloading Java on desktop or server grade GPUs and not on embedded GPUs. Our research is focussed on providing a framework for accelerating Java programs on embedded GPUs. Our experiments were conducted on a Freescale i.MX6Q SabreLite board which encompasses a quad-core ARM Cortex A9 CPU and a Vivante GC 2000 GPU that supports the OpenCL 1.1 Embedded Profile. We successfully accelerated Java code and reduced energy consumption by employing two approaches, namely JNI-OpenCL, and JOCL, which is a popular Java-binding for OpenCL. These approaches can be easily implemented on other platforms by embedded Java programmers to exploit the computational power of GPUs. Our results show up to an 8 times increase in performance efficiency and 3 times decrease in energy consumption compared to the embedded CPU-only execution of Java program. To the best of our knowledge, this is the first work done on accelerating Java on an embedded GPU.