Understanding performance bottlenecks of applications in high-performance computing can lead to dramatic improvements in their performances. For example, a key problem in GPU programming is finding performance bottlenecks and solving them to reach the best possible performance. These bottlenecks in GPU architectures include a number of factors such as memory access latency, branch divergence, utilization, and the amount of existing parallelism. In addition, a simple profiling cannot demonstrate the relations between these bottlenecks. These are the publications related to Performance Modeling.

• S. Ali Mirsoleimani, Farshad Khunjush, and Ali Karami. 2015. "A two-tier design space exploration algorithm to construct GPU performance model". J. Syst. Archit. 61, 10 (November 2015), 576-583. DOI: https://doi.org/10.1016/j.sysarc.2015.07.006
• Ali Karami, Farshad Khunjush, and Seyyed Ali Mirsoleimani. 2015. "A statistical performance analyzer framework for OpenCL kernels on Nvidia GPUs". J. Supercomput. 71, 8 (August 2015), 2900-2921. DOI=http://dx.doi.org/10.1007/s11227-014-1338-z
• S. Ali Mirsoleimani, Ali Karami, and Farshad Khunjush. 2014. "A Two-Tier Design Space Exploration Algorithm to Construct a GPU Performance Predictor". In Proceedings of the 27th International Conference on Architecture of Computing Systems — ARCS 2014 - Volume 8350, Erik Maehle, Kay Römer, Wolfgang Karl, and Eduardo Tovar (Eds.), Vol. 8350. Springer-Verlag, Berlin, Heidelberg, 135-146. DOI: https://doi.org/10.1007/978-3-319-04891-8_12
• R. Mokhtari, A. Abbasi, F. Khunjush and R. Azimi, ”Soren: Adaptive MapReduce for Programmable GPUs,” Proceedings, the 4th International Workshop on Programmability Issues for Heterogeneous Multicores (MULTIPROG 2011), held in conjunction with Hipeac 2011, Heraklion, Crete, Greece, January 23, 2011.

Burgeoning market for cloud applications has brought significant interest for cloud providers. They look for methods to maximize their profit margins by executing higher number of jobs while minimizing the operational costs and environmental footprints. Energy consumption of servers plays a crucial role in operational costs. There have been different methods to deal with energy consumption from hardware level to higher layers of abstractions such as compilers and operating systems.

• Seyedmehdi Hosseinimotlagh, Farshad Khunjush, and Rashidaldin Samadzadeh. 2015. "SEATS: smart energy-aware task scheduling in real-time cloud computing". J. Supercomput. 71, 1 (January 2015), 45-66. DOI=http://dx.doi.org/10.1007/s11227-014-1276-9
• Seyedmehdi Hosseinimotlagh, Farshad Khunjush, and Seyedmahyar Hosseinimotlagh. 2014. "Migration-less Energy-Aware Task Scheduling Policies in Cloud Environments". In Proceedings of the 2014 28th International Conference on Advanced Information Networking and Applications Workshops (WAINA '14). IEEE Computer Society, Washington, DC, USA, 391-397. DOI: https://doi.org/10.1109/WAINA.2014.66
• Seyedmehdi Hosseinimotlagh, Farshad Khunjush, and Seyedmahyar Hosseinimotlagh. 2014. "A Cooperative Two-Tier Energy-Aware Scheduling for Real-Time Tasks in Computing Clouds". In Proceedings of the 2014 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP '14). IEEE Computer Society, Washington, DC, USA, 178-182. DOI: https://doi.org/10.1109/PDP.2014.91

Data sets grow rapidly- in part because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.

• Javad Dogani, Ali Yazdanpanah, Arash Zare, and Farshad Khunjush, "A two-tier multi-objective service placement in container-based fog-cloud computing platforms," Journal of Cluster Computing, Volum 27, Issue 4, pp. 4491-4514, 2024.
• Javad Dogani and Farshad Khunjush, "Proactive auto-scaling technique for web applications in container-based edge computing using federated learning model," Journal of Parallel and Distributed Computing, Volume 187, pp. 104837-, 2024.
• M. Seydali, F. Khunjush, and J. Dogani, "Streaming traffic classification: a hybrid deep learning and big data approach," Journal of Cluster Computing, pp. 1-29, 2024.
• M. Seydali, F. Khunjush, B. Akbari and J. Dogani, "CBS: A Deep Learning Approach for Encrypted Traffic Classification With Mixed Spatio-Temporal and Statistical Features," IEEE Access, Volume 11, pp. 141674-141702, 2023.
• Javad Dogani, Reza Namvar, and Farshad Khunjush, "Auto-scaling techniques in container-based cloud and edge/fog computing: Taxonomy and survey," Journal of Computer Communications, Volume 209, pp. 120-150, 2023.
• Javad Dogani, Farshad Khunjush, Mohammad Reza Mahmoudi, and Mehdi Seydali, "Multivariate workload and resource prediction in cloud computing using CNN and GRU by attention mechanism," The Journal of Supercomputing, Volum 79, Issue 3, pp. 3437-3470, 2023.
• Javad Dogani, Farshad Khunjush, and Mehdi Seydali, "Host load prediction in cloud computing with Discrete Wavelet Transformation (DWT) and Bidirectional Gated Recurrent Unit (BiGRU) network," Journal of Computer Communications, Volume 198, pp. 157-174, 2023.
• Javad Dogani, Farshad Khunjush, and Mehdi Seydali, "K-AGRUED: A Container Autoscaling Technique for Cloud-based Web Applications in Kubernetes Using Attention-based GRU Encoder-Decoder," Journal of Grid Computing, Volume 20, Issue 4, pp. 1-32, 2022.
• Bahram Yarahmadi and Farshad Khunjush, "Bamshad: A JIT Compiler for running Java Stream APIs on heterogeneous environments", The 19th CSI International Symposium on Computer Architecture & Digital Systems (CADS 2017), Kish Island, Iran, December 21-22, 2017.
• Sayyed Ali Mirsoleimani, Ali Karami, and Farshad Khunjush. 2013. "A parallel memetic algorithm on GPU to solve the task scheduling problem in heterogeneous environments". In Proceedings of the 15th annual conference on Genetic and evolutionary computation (GECCO '13), Christian Blum (Ed.). ACM, New York, NY, USA, 1181-1188. DOI: https://doi.org/10.1145/2463372.2463518
• Masoud Ebrahimi and Farshad Khunjush, ”Second Level Parallelism using SIMD Accelerators on Heterogeneous MapReduce Clusters,” Proceedings the 24th IASTED International Conference on Parallel and Distributed Computing and Systems (PDCS 2012), USA, November 12-14, 2012.
• Amin Abbasi, Farshad Khunjush, and Reza Azimi, ”A Preliminary Study of Incorporating GPUs in the Hadoop Framework,” Proceedings, 16th International Symposium on Computer Architecture & Digital Systems (CADS 2012), Iran, May 2012.

The way in which measures from environment are taken has changed. In the past, different sensors close to the point of interest were placed and wired to a central station where the processing was carried out. Currently, the new paradigm of massive measurement is represented by wireless sensor networks. In this new scenario, distributed tiny nodes measure parameters and send the information wirelessly and even process data locally, performing distributed computing in the network. These nodes are autonomous and nonintrusive.

• Saeed Mehrjoo and Farshad Khunjush. 2018. "Accurate compressive data gathering in wireless sensor networks using weighted spatio-temporal compressive sensing". Telecommun. Syst. 68, 1 (May 2018), 79-88. DOI: https://doi.org/10.1007/s11235-017-0376-2
• Saeid Mehrjoo, Farshad Khunjush, ”Optimal data aggregation tree in wireless sensor networks based on improved river formation dynamics,” Accepted for publication in Computational Intelligence.