<p>Over the last three decades, computer architects have been able to achieve an increase in performance for single processors by, e.g., increasing clock speed, introducing cache memories and using instruction level parallelism. However, because of power consumption and heat dissipation constraints, this trend is going to cease. In recent times, hardware engineers have instead moved to new chip architectures with multiple processor cores on a single chip. With multi-core processors, applications can complete more total work than with one core alone. To take advantage of multi-core processors, we have to develop parallel applications that assign tasks to different cores. On each core, pipeline, data and task parallelization can be used to achieve higher performance. Dataflow programming languages are attractive for achieving parallelism because of their high-level, machine-independent, implicitly parallel notation and because of their fine-grain parallelism. These features are essential for obtaining effective, scalable utilization of multi-core processors.</p><p>In this thesis work we have parallelized an existing audio codec - Adaptive Multi-Rate Wide Band (AMR-WB) - written in the C language for single core processor. The target platform is a multi-core AMR11 MP developer board. The final result of the efforts is a working AMR-WB encoder implemented in CAL and running in the OpenDF simulator. The C specification of the AMR-WB encoder was analysed with respect to dataflow and parallelism. The final implementation was developed in the CAL Actor Language, with the goal of exposing available parallelism - different dataflows - as well as removing unwanted data dependencies. Our thesis work discusses mapping techniques and guidelines that we followed and which can be used in any future work regarding mapping C based applications to CAL. We also propose solutions for some specific dependencies that were revealed in the AMR-WB encoder analysis and suggest further investigation of possible modifications to the encoder to enable more efficient implementation on a multi-core target system.</p>

Over the last three decades, computer architects have been able to achieve an increase in performance for single processors by, e.g., increasing clock speed, introducing cache memories and using instruction level parallelism. However, because of power consumption and heat dissipation constraints, this trend is going to cease. In recent times, hardware engineers have instead moved to new chip architectures with multiple processor cores on a single chip. With multi-core processors, applications can complete more total work than with one core alone. To take advantage of multi-core processors, we have to develop parallel applications that assign tasks to different cores. On each core, pipeline, data and task parallelization can be used to achieve higher performance. Dataflow programming languages are attractive for achieving parallelism because of their high-level, machine-independent, implicitly parallel notation and because of their fine-grain parallelism. These features are essential for obtaining effective, scalable utilization of multi-core processors. In this thesis work we have parallelized an existing audio codec - Adaptive Multi-Rate Wide Band (AMR-WB) - written in the C language for single core processor. The target platform is a multi-core AMR11 MP developer board. The final result of the efforts is a working AMR-WB encoder implemented in CAL and running in the OpenDF simulator. The C specification of the AMR-WB encoder was analysed with respect to dataflow and parallelism. The final implementation was developed in the CAL Actor Language, with the goal of exposing available parallelism - different dataflows - as well as removing unwanted data dependencies. Our thesis work discusses mapping techniques and guidelines that we followed and which can be used in any future work regarding mapping C based applications to CAL. We also propose solutions for some specific dependencies that were revealed in the AMR-WB encoder analysis and suggest further investigation of possible modifications to the encoder to enable more efficient implementation on a multi-core target system.

The high demand of the video decoding has pushed the developers to implement the decoders on parallel architectures. This thesis provides the deliberations about the implementation of an MPEG-4 decoder on a massively parallel processor array (MPPA), Ambric 2045, by converting the CAL actor language implementation of the decoder. This decoder is the Xilinx model of the MPEG-4 Simple Profile decoder and consists of four main blocks; parser, acdc, idct2d and motion. The parser block is developed in another thesis work [20] and the rest of the decoder, which consists of the other three blocks, is implemented in this thesis work. Afterwards, in order to complete the decoder, the parser block is combined with the other three blocks. Several methods are developed for conversion purposes. Additionally, a number of other methods are developed in order to overcome the constraints of the ambric architecture such as no division support. At the beginning, for debugging purposes, the decoder is implemented on a simulator which is designed for Ambric architecture. Finally the implementation is uploaded to the Ambric 2045 chip and tested with different input streams. The performance of the implementation is analyzed and satisfying results are achieved when compared to the standards which are in use in the market. These performance results can be considered as satisfying for any real-time application as well. Furthermore, the results are compared with the results of the CAL implementation, running on a single 2GHz i7 intel processor, in terms of speed and efficiency. The Ambric implementation runs 4,7 times faster than the CAL implementation when a small input stream (300 frames with resolution of 176x144) is used. However, when a large input stream (384 frames with resolution of 720x480) is used, the Ambric implementation shows a performance which is approximately 32 times better than the CAL implementation, in terms of decoding speed and throughput. The performance may increase further together with the size of the input stream up to some point.