Performance Debugging and Tuning using an Instruction-Set Simulator

Instruction-set simulators allow programmers a detailed level of insight into, and control over, the execution of a program, including parallel programs and operating systems. In principle, instruction set simulation can model any target computer and gather any statistic. Furthermore, such simulators are usually portable, independent of compiler tools, and deterministic-allowing bugs to be recreated or measurements repeated. Though often viewed as being too slow for use as a general programming tool, in the last several years their performance has improved considerably. We describe SIMICS, an instruction set simulator of SPARC-based multiprocessors developed at SICS, in its rôle as a general programming tool. We discuss some of the benefits of using a tool such as SIMICS to support various tasks in software engineering, including debugging, testing, analysis, and performance tuning. We present in some detail two test cases, where we've used SimICS to support analysis and performance tuning of two applications, Penny and EQNTOTT. This work resulted in improved parallelism in, and understanding of, Penny, as well as a performance improvement for EQNTOTT of over a magnitude. We also present some early work on analyzing SPARC/Linux, demonstrating the ability of tools like SimICS to analyze operating systems

Performance Analysis of a Novel GPU Computation-to-core Mapping Scheme for Robust Facet Image Modeling

Though the GPGPU concept is well-known in image processing, much more work remains to be done to fully exploit GPUs as an alternative computation engine. This paper investigates the computation-to-core mapping strategies to probe the efficiency and scalability of the robust facet image modeling algorithm on GPUs. Our fine-grained computation-to-core mapping scheme shows a significant performance gain over the standard pixel-wise mapping scheme. With in-depth performance comparisons across the two different mapping schemes, we analyze the impact of the level of parallelism on the GPU computation and suggest two principles for optimizing future image processing applications on the GPU platform

A low-cost high-speed twin-prefetching DSP-based shared-memory system for real-time image processing applications

This dissertation introduces, investigates, and evaluates a low-cost high-speed twin-prefetching DSP-based bus-interconnected shared-memory system for real-time image processing applications. The proposed architecture can effectively support 32 DSPs in contrast to a maximum of 4 DSPs supported by existing DSP-based bus- interconnected systems. This significant enhancement is achieved by introducing two small programmable fast memories (Twins) between the processor and the shared bus interconnect. While one memory is transferring data from/to the shared memory, the other is supplying the core processor with data. The elimination of the traditional direct linkage of the shared bus and processor data bus makes feasible the utilization of a wider shared bus i.e., shared bus width becomes independent of the data bus width of the processors. The fast prefetching memories and the wider shared bus provide additional bus bandwidth into the system, which eliminates large memory latencies; such memory latencies constitute the major drawback for the performance of shared-memory multiprocessors. Furthermore, in contrast to existing DSP-based uniprocessor or multiprocessor systems the proposed architecture does not require all data to be placed on on-chip or off-chip expensive fast memory in order to reach or maintain peak performance. Further, it can maintain peak performance regardless of whether the processed image is small or large. The performance of the proposed architecture has been extensively investigated executing computationally intensive applications such as real-time high-resolution image processing. The effect of a wide variety of hardware design parameters on performance has been examined. More specifically tables and graphs comprehensively analyze the performance of 1, 2, 4, 8, 16, 32 and 64 DSP-based systems, for a wide variety of shared data interconnect widths such as 32, 64, 128, 256 and 512. In addition, the effect of the wide variance of temporal and spatial locality (present in different applications) on the multiprocessor\u27s execution time is investigated and analyzed. Finally, the prefetching cache-size was varied from a few kilobytes to 4 Mbytes and the corresponding effect on the execution time was investigated. Our performance analysis has clearly showed that the execution time converges to a shallow minimum i.e., it is not sensitive to the size of the prefetching cache. The significance of this observation is that near optimum performance can be achieved with a small (16 to 300 Kbytes) amount of prefetching cache

Strengthening measurements from the edges: application-level packet loss rate estimation

Network users know much less than ISPs, Internet exchanges and content providers about what happens inside the network. Consequently users cannot either easily detect network neutrality violations or readily exercise their market power by knowledgeably switching ISPs. This paper contributes to the ongoing efforts to empower users by proposing two models to estimate -- via application-level measurements -- a key network indicator, i.e., the packet loss rate (PLR) experienced by FTP-like TCP downloads. Controlled, testbed, and large-scale experiments show that the Inverse Mathis model is simpler and more consistent across the whole PLR range, but less accurate than the more advanced Likely Rexmit model for landline connections and moderate PL

Scalability of broadcast performance in wireless network-on-chip

Networks-on-Chip (NoCs) are currently the paradigm of choice to interconnect the cores of a chip multiprocessor. However, conventional NoCs may not suffice to fulfill the on-chip communication requirements of processors with hundreds or thousands of cores. The main reason is that the performance of such networks drops as the number of cores grows, especially in the presence of multicast and broadcast traffic. This not only limits the scalability of current multiprocessor architectures, but also sets a performance wall that prevents the development of architectures that generate moderate-to-high levels of multicast. In this paper, a Wireless Network-on-Chip (WNoC) where all cores share a single broadband channel is presented. Such design is conceived to provide low latency and ordered delivery for multicast/broadcast traffic, in an attempt to complement a wireline NoC that will transport the rest of communication flows. To assess the feasibility of this approach, the network performance of WNoC is analyzed as a function of the system size and the channel capacity, and then compared to that of wireline NoCs with embedded multicast support. Based on this evaluation, preliminary results on the potential performance of the proposed hybrid scheme are provided, together with guidelines for the design of MAC protocols for WNoC.Peer ReviewedPostprint (published version