- Associate Professor, Electrical and Computer Engineering
- Associate Professor, BIO5 Institute
Ali Akoglu is an Associate Professor in the Department of Electrical and Computer Engineering and the BIO5 Institute at the University of Arizona. He is the site-director of the National Science Foundation (NSF), Industry-University Cooperative Research Center on Cloud and Autonomic Computing regarding the design and development of architectures for achieving self-management capabilities across the layers of cloud computing systems, director of the NVIDIA CUDA Teaching Center for promoting the GPU based computing across the UA campus, and the director of the Reconfigurable Computing Laboratory on design and development of adaptive hardware architectures and self-configurable architectures for reusable systems. He received his Ph.D. degree in Computer Science from the Arizona State University in 2005. His research program focuses on high performance computing systems and non-traditional computing architectures with themes that cover: a) development of resource management strategies from multi-processor system-on-chip to cloud computing scale; b) design and development of reconfigurable hardware architectures for reusable systems; c) modeling and simulation of neuromorphic computing architectures; and d) restructuring computationally challenging algorithms for achieving high performance on field programmable gate array (FPGA) and graphics processing unit (GPU) hardware architectures. He has been involved in many crosscutting collaborative projects with the goal of solving the challenges of bridging the gap between the domain scientist, programming environment and emerging highly-parallel hardware architectures. His research projects have been funded by the National Science Foundation, Defense Advanced Research Projects Agency, Office of Naval Research, US Air Force, NASA Jet Propulsion Laboratories, Army Battle Command Battle Laboratory, and industry partners such as Nvidia and Raytheon.
- Ph.D. Computer Science
- Arizona State University, Tempe, Arizona
- Application Specific Reconfigurable Architecture Design Methodology
- B.S. Computer Engineering
- Purdue University, West Lafayette, Indiana
- The University of Arizona, Tucson, Arizona (2013 - Ongoing)
- The University of Arizona, Tucson, Arizona (2005 - 2012)
Computer Architecture, Reconfigurable Computing, High Performance Computing
Adaptive Hardware Systems, Reconfigurable Architectures, Computer Aided Design Tools for Field Programmable Gate Arrays, Scientific Computing
DissertationECE 920 (Spring 2019)
High-Performance ComputECE 569 (Spring 2019)
Directed ResearchECE 492 (Fall 2018)
DissertationECE 920 (Fall 2018)
Fund of Computer OrganizationECE 369A (Fall 2018)
ThesisECE 910 (Fall 2018)
DissertationECE 920 (Spring 2018)
Reconfigurable ComputingECE 506 (Spring 2018)
Digital LogicECE 274A (Fall 2017)
DissertationECE 920 (Fall 2017)
Fund of Computer OrganizationECE 369A (Fall 2017)
ResearchECE 900 (Fall 2017)
ThesisECE 910 (Fall 2017)
Directed ResearchECE 392 (Spring 2017)
Directed ResearchECE 492 (Spring 2017)
DissertationECE 920 (Spring 2017)
High-Performance ComputECE 569 (Spring 2017)
Independent StudyECE 599 (Spring 2017)
ResearchECE 900 (Spring 2017)
ThesisECE 910 (Spring 2017)
DissertationECE 920 (Fall 2016)
Fund of Computer OrganizationECE 369A (Fall 2016)
ResearchECE 900 (Fall 2016)
- Machovec, D., Khemka, B., Kumbhare, N., Pasricha, S., Maciejewski,, A. A., Siegel, H. J., Akoglu, A., Koenig, G. A., Hariri, S. A., Tunc, C., Wright, M., Hilton, M., Rambharos, R., Blandin, C., Fargo, F., Louri, A., & Imam, N. (2017). Utility- Based Resource Management in an Oversubscribed Energy-Constrained Heterogeneous Environment Executing Parallel Applications. Journal of Parallel Computing. doi:10.1016/j.parco.2017.11.005
- Tunc, C., Kumbhare, N., Akoglu, A., Hariri, S. A., & Machovec, D. (2016). Value of Service Based Task Scheduling for Cloud Computing Systems. IEEE International Conference on Cloud and Autonomic Computing (ICCAC), 1-11.
- Tunc, C., Machovec, D., Kumbhare, N., Akoglu, A., Hariri, S. A., Khemka, B., & Siegel, H. J. (2017). Value of Service Based Resource Management for Large-Scale Computing Systems. Cluster Computing, 1-18. doi:10.1007/s10586-017-0901-9
- Vincent, B., Buntzman, A., Hopson, B., McEwen, C., Cowell, L., Akoglu, A., Zhang, H., & Frelinger, J. (2016). iWAS-A novel approach to analyzing Next Generation Sequence data for immunology. Cellular Immunology, 299, 6-13. doi:http://dx.doi.org/10.1016/j.cellimm.2015.10.012
- Leow, Y. K., Akoglu, A., & Lysecky, S. (2013). An Analytical Model for Evaluating Static Power of Homogeneous FPGA Architectures. ACM Transactions on Reconfigurable Technology and Systems (TRETS), 6(4), 18.
- Liu, H., Rajavel, S. T., & Akoglu, A. (2013). Integration of Net-Length Factor with Timing-and Routability-Driven Clustering Algorithms. ACM Transactions on Reconfigurable Technology and Systems (TRETS), 6(3), 12.
- Song, Y., & Akoglu, A. (2013). An adaptive motion estimation architecture for H. 264/AVC. Journal of Signal Processing Systems, 73(2), 161--179.
- Benkrid, K., Akoglu, A., Ling, C., Song, Y., Liu, Y., & Tian, X. (2012). High performance biological pairwise sequence alignment: FPGA versus GPU versus cell BE versus GPP. International Journal of Reconfigurable Computing, 2012, 7.
- Nimmagadda, V. K., Akoglu, A., Hariri, S., & Moukabary, T. (2012). Cardiac simulation on multi-GPU platform. The Journal of Supercomputing, 59(3), 1360--1378.
- Song, Y., & Akoglu, A. (2012). Bit-by-bit pipelined and hybrid-grained 2d architecture for motion estimation of h. 264/avc. Journal of Signal Processing Systems, 68(1), 49--62.
- Ditzler, G., Hariri, S. A., & Akoglu, A. (2017, Spring). High Performance Machine Learning (HPML) Framework to Support DDDAS Decision Support Systems: Design Overview. In 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems (FAS*W), Tucson, AZ, 360-362.
- Esmaili, E., Akoglu, A., Ditzler, G., Hariri, S. A., Szep, J., & Moukabary, T. (2017, September). Autonomic Management of 3D Cardiac Simulations (Best Paper Award). In IEEE International Conference on Cloud and Autonomic Computing (ICCAC), Tucson, AZ, 1-9.
- Ghaffari, F., Akoglu, A., & Vasic, B. V. (2017, August). Multi-mode Low-latency Software-defined Error Correction for Data Centers (Invited Paper). In 26-th International Conf. Comp. Comm. Networks (ICCCN 2017).
- Ghaffari, F., Unal, B., Akoglu, A., Le, K., Declercq, D., & Vasic, B. V. (2017, December 1-5). Efficient FPGA Implementation of Probabilistic Gallager B LDPC Decoder. In 24th IEEE Intl. Conf. on Electronics, Circuits and Systems (ICECS).
- Gianelli, S., Richter, E., Jimenez, D., Valdez, H., Adegbija, T., & Akoglu, A. (2017, September). Application-Specific Autonomic Cache Tuning for General Purpose GPUs. In IEEE International Conference on Cloud and Autonomic Computing (ICCAC), Tucson, AZ, 104-113.
- Kumbhare, N., Tunc, C., Machovec, D., Akoglu, A., Hariri, S. A., & Siegel, H. J. (2017, September). Value-Based Scheduling for Oversubscribed Power-Constrained Homogeneous HPC Systems. In International Conference on Cloud and Autonomic Computing (ICCAC), Tucson, USA, 120-130.
- Unal, B., Ghaffari, F., Akoglu, A., & Vasic, B. V. (2017, May). Analysis and Implementation of Resource Efficient Probabilistic LDPC Decoder: Trade-offs Between the Decoding Performance and Hardware Performance. In IEEE International Symposium on Circuits and Systems (ISCAS).
- Yang, M., Djordjevic, I. B., Tunc, C., Hariri, S. A., & Akoglu, A. (2017, December). Integrated Optical Network-On-Chips for Dynamically Composable Data Center. In IEEE International Conference on High Performance Computing and Communications (HPCC), Bangkok, Thailand, 1-8.
- Gu, S., Yao, L., Tunc, C., Akoglu, A., Hariri, S. A., & Richie, E. (2016, September). An Autonomic Workflow Performance Manager for Weather Research and Forecast Workflows. In IEEE International Conference on Cloud and Autonomic Computing (ICCAC), 111-114.
- Kumbhare, N., Tunc, C., Hariri, S. A., Djordjevic, I. B., Akoglu, A., & Siegel, H. J. (2016, Nov-Dec). Just In Time Architecture (JITA) for Dynamically Composable Data Centers. In 13th ACS/IEEE International Conference on Computer Systems and Applications AICCSA, 8 pages.
- Machovec, D., Tunc, C., Kumbhare, N., Khemka, B., Akoglu, A., Hariri, S. A., & Siegel, H. J. (2016, May). Value-Based Resource Management in High-Performance Computing Systems. In 7th Workshop on Scientific Cloud Computing (ScienceCloud 2016), The 25th International Symposium on High Performance Parallel and Distributed Computing (HPDC '16), 19-26.
- Unal, B., & Akoglu, A. (2016, Fall). Resource Efficient Real-Time Processing of Contrast Limited Adaptive Histogram Equalization. In 26th International Conference on Field-Programmable Logic and Applications (FPL), 1-8.
- Bidyanta, N., Akoglu, A., Vanhoy, G. M., Hirzallah, M., Bose, T., & Ryu, B. (2015, March). GPU and FPGA Based Architecture Design for Real-time Signal Classification. In In Proceedings of the 2015 Wireless Innovation Forum Conference on Wireless Communications Technologies and Software Defined Radio (WInnComm’15), 70-79.
- Fargo, F., Tunc, C., Al-Nashif, Y., Akoglu, A., & Hariri, S. (2014, September). Autonomic Workload and Resources Management of Cloud Computing Resources. In IEEE International Conference on Cloud and Autonomic Computing (ICCAC’14), 101-110.
- Gadfort, P., Dasu, A., Akoglu, A., Leow, Y. K., & Fritze, M. (2014, September). A Power Efficient Reconfigurable System-in-Stack: 3D integration of accelerators, FPGAs, and DRAM. In System-on-Chip Conference (SOCC), 2014 27th IEEE International, 11-16.
- Striemer, G., Krovi, H., Akoglu, A., Vincent, B., Hopson, B., Frelinger, J., & Buntzman, A. (2014, May). Overcoming the Limitations Posed by TCR-beta Repertoire Modeling through a GPU-Based In-Silico DNA Recombination Algorithm. In IEEE 28th International Parallel and Distributed Processing Symposium, 231-240.
- Gupta, P., Akoglu, A., Melde, K., & Roveda, J. (2013). FPGA based single cycle, reconfigurable router for NoC applications. In Circuits and Systems (ISCAS), 2013 IEEE International Symposium on, 2428--2431.
- Leow, Y. K., & Akoglu, A. (2013). A Hybrid FPGA Model to Estimate On-Chip Crossbar Logic Utilization in SoC Platforms. In Parallel and Distributed Processing Symposium Workshops \& PhD Forum (IPDPSW), 2013 IEEE 27th International, 239--246.
- Bailey, P. E., Patki, T., Striemer, G. M., Akoglu, A., Lowenthal, D. K., Bradbury, P., Vaughn, M., Wang, L., & Goff, S. (2012). Quantitative Trait Locus Analysis Using a Partitioned Linear Model on a GPU Cluster. In Parallel and Distributed Processing Symposium Workshops \& PhD Forum (IPDPSW), 2012 IEEE 26th International, 752--760.
- Ch\'avez, R. S., Rajavel, S. T., & Akoglu, A. (2012). WL-Emap: Wirelength prediction based technology mapping for FPGAs. In Programmable Logic (SPL), 2012 VIII Southern Conference on, 1--6.
- Bidyanta, N., & Akoglu, A. (2015, March). RealTime GPU Based Video Segmentation with Depth Information. GPU Technology Conference. San Jose, California.
- Buntzman, A., & Akoglu, A. (2016, September). Grand Challenge: Mapping the Human Immune System. UA News, The Daily Wildcat, AZ PBS. https://uanews.arizona.edu/story/grand-challenge-mapping-human-immune-systemMore infoSeveral other resources picked up this news and adopted:CYVERSE News, Sept. 28, 2016 “Mapping The Human Immune System” http://www.cyverse.org/news/mapping-human-immune-systemThe Daily Wildcat, October 20, 2016, UA collaboration leads to immune system mapping http://www.wildcat.arizona.edu/article/2016/10/ua-collaboration-leads-to-immune-system-mappingAZ PBS, October 25, 2016, Mapping the Human Immune Systemhttp://www.azpbs.org/arizonahorizon/play.php?vidId=9639http://deptmedicine.arizona.edu/news/2016/cyverse-explores-complexities-mapping-human-immune-systemhttp://medicine.arizona.edu/news/2016/grand-challenge-mapping-human-immune-systemhttps://www.technologynetworks.com/tn/news/mapping-the-human-immune-system-200216https://www.laboratoryequipment.com/news/2016/10/grand-challenge-mapping-human-immune-systemhttp://www.futurity.org/genetic-map-immune-system-1262192-2/https://www.technology.org/2016/10/14/supercomputers-improve-cancer-diagnostics/http://uacc.arizona.edu/news/supercomputers-could-improve-cancer-diagnosticshttp://www.scientificcomputing.com/news/2016/10/supercomputers-could-improve-cancer-diagnosticshttps://exceptionmag.com/28932/software-maps-immune-system-in-17-days-not-106-years/