Browsing by Author "Mustafa, Naveed Ul"

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    Adaptive routing framework for network on chip architectures
    (ACM, 2016-01) Mustafa, Naveed Ul; Öztürk, Özcan; Niar, S.
    In this paper we suggest and demonstrate the idea of applying multiple routing algorithms during the execution of a real application mapped on a Network-on-Chip (NoC). Traffic pattern of a real application may change during its execution. As performance of an algorithm depends on the traffic pattern, using the same routing algorithm for the entire span of execution may be inefficient. We study the feasibility of this idea for applications such as SPARSE and MPEG-4 decoder, by applying different routing algorithms. By applying more than one routing algorithms, throughput improves up to 17.37% and 6.74% in the case of SPARSE and MPEG-4 decoder applications, respectively, as compared to the application of single routing algorithm. © 2016 ACM.
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    Exploiting architectural features of a computer vision platform towards reducing memory stalls
    (Springer, 2020) Mustafa, Naveed Ul; O’Riordan, M. J.; Rogers, S.; Öztürk, Özcan
    Computer vision applications are becoming more and more popular in embedded systems such as drones, robots, tablets, and mobile devices. These applications are both compute and memory intensive, with memory bound stalls (MBS) making a significant part of their execution time. For maximum reduction in memory stalls, compilers need to consider architectural details of a platform and utilize its hardware components efficiently. In this paper, we propose a compiler optimization for a vision-processing system through classification of memory references to reduce MBS. As the proposed optimization is based on the architectural features of a specific platform, i.e., Myriad 2, it can only be applied to other platforms having similar architectural features. The optimization consists of two steps: affinity analysis and affinity-aware instruction scheduling. We suggest two different approaches for affinity analysis, i.e., source code annotation and automated analysis. We use LLVM compiler infrastructure for implementation of the proposed optimization. Application of annotation-based approach on a memory-intensive program shows a reduction in stall cycles by 67.44%, leading to 25.61% improvement in execution time. We use 11 different image-processing benchmarks for evaluation of automated analysis approach. Experimental results show that classification of memory references reduces stall cycles, on average, by 69.83%. As all benchmarks are both compute and memory intensive, we achieve improvement in execution time by up to 30%, with a modest average of 5.79%.
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    Implications of non-volatile memory as primary storage for database management systems
    (IEEE, 2017) Mustafa, Naveed Ul; Armejach, A.; Öztürk, Özcan; Cristal, A.; Unsal, O. S.
    Traditional Database Management System (DBMS) software relies on hard disks for storing relational data. Hard disks are cheap, persistent, and offer huge storage capacities. However, data retrieval latency for hard disks is extremely high. To hide this latency, DRAM is used as an intermediate storage. DRAM is significantly faster than disk, but deployed in smaller capacities due to cost and power constraints, and without the necessary persistency feature that disks have. Non-Volatile Memory (NVM) is an emerging storage class technology which promises the best of both worlds. It can offer large storage capacities, due to better scaling and cost metrics than DRAM, and is non-volatile (persistent) like hard disks. At the same time, its data retrieval time is much lower than that of hard disks and it is also byte-addressable like DRAM. In this paper, we explore the implications of employing NVM as primary storage for DBMS. In other words, we investigate the modifications necessary to be applied on a traditional relational DBMS to take advantage of NVM features. As a case study, we have modified the storage engine (SE) of PostgreSQL enabling efficient use of NVM hardware. We detail the necessary changes and challenges such modifications entail and evaluate them using a comprehensive emulation platform. Results indicate that our modified SE reduces query execution time by up to 40% and 14.4% when compared to disk and NVM storage, with average reductions of 20.5% and 4.5%, respectively. © 2016 IEEE.