Dr. Dingwen Tao from the School of Electrical Engineering and Computer Science at Washington State University invites applications for Ph.D. studies in the areas of high-performance computing, parallel and distributed systems, and big data analytics. Students will be fully funded by full research and/or teaching assistantship. Dr. Tao has published many papers in top HPC and Big Data conferences and journals, including ACM HPDC, ACM ICS, ACM ICPP, ACM PPoPP, ACM PACT, IEEE/ACM SC, IEEE BigData, IEEE CLUSTER, IEEE DAC, IEEE IPDPS, IEEE Transactions on Parallel and Distributed Systems (TPDS), IJHPCA, etc. His research has been supported by the National Science Foundation, Department of Energy, National Oceanic and Atmospheric Administration, and Xilinx.
Qualified candidates are expected to have strong programming experiences in C/C++ and Python under Linux platform and good English writing skills. Students with prior research experience in parallel computing (MPI/OpenMP) and/or acceleration technologies (GPGPU/FPGA) are preferred. Students with strong CS, Math, EE, or Physics backgrounds are especially encouraged to apply. Applicants should send their detailed CVs, all transcripts, G/T scores, and other supporting documents in one pdf file directly to Dr. Tao at dingwen.tao@wsu.edu and apply online at https://gradschool.wsu.edu/apply/ 120 days before the beginning of the semester for full consideration. Please indicate Dr. Tao as your potential advisor in your statement of purpose.
Note that WSU EECS has removed the GRE requirement for Spring, Summer, Fall 2021 admits and TOFEL requirement is only 93 for Computer Science.
Why WSU EECS?
Ranking: WSU is an R1 public research university and has high CS rankings:
- According to Washington Monthly, WSU ranks #47 in the list of top national public universities in U.S.
- According to US News Rankings, WSU CS graduate program ranks #75 in U.S.
- According to CS Rankings, WSU ranks #7 in design automation, #17 in HPC, #54 in database, #57 in AI, #61 in all system areas, and #74 in all areas in U.S.
Faculty: 90% of WSU EECS junior faculty members have received prestigious federal young investigator awards, such as NSF/DOE/DOD Early Career Award. Students in EECS also won highly competitive PhD fellowships such as Microsoft PhD Fellowship.
Job Opportunity: WSU is also close to Pacific Northwest National Laboratory (PNNL) and has a joint program with PNNL, providing many collaboration opportunities with high-impact, national science and engineering research, especially HPC. Dr Tao's group has close collaborations with many national research laboratories such as Argonne National Lab, Pacific Northwest National Lab, Los Alamos National Lab, Oak Ridge National Lab to develop emerging research and technology into real-world applications. Most PhD students have opportunities to intern at these national labs to work on fantastic collaborative projects.
Location: WSU is located in Pullman close to several big cities including Seattle, Portland, and Boise, which have many giant IT companies such as Amazon, Microsoft, Boeing, Intel, Micron. Living cost is quite reasonable. Pullman has generally nice climates, safe neighborhoods, and beautiful outdoors/scenery. Graduate students can enjoy an excellent quality of life.
Why HiPDAC Group?
The High Performance Data Analytics and Computing (HiPDAC) lab conducts research and development in the broad area of high-performance computing and big data analytics. The lab's mission is to develop techniques, design algorithms, and build software to improve the performance, reliability, and energy efficiency for large-scale computations and big data applications. The current research topics include (but not limited to) accelerator-based computing (GPU/FPGA), scientific data management, data compression, fault tolerance and resilience, energy-efficient computing, numerical algorithms and software, scientific simulations, and large-scale machine learning.
Some recently funded research projects:
1. A COMPRESSION-SUPPORTED EFFICIENT DEEP LEARNING TRAINING FRAMEWORK FOR LARGE-SCALE NEURAL NETWORKS
Abstract: Deep learning (DL) has rapidly evolved to a state-of-the-art technique in many science and technology disciplines, such as scientific exploration, national security, smart environment, and healthcare. Many of these DL applications require using HPC resources to process large amounts of data. For example, researchers and scientists are employing extreme-scale DL applications in HPC infrastructures to classify extreme weather patterns and high-energy particles. In recent years, using GPUs to accelerate DL applications has attracted increasing attention. However, the ever-increasing scales of DL applications bring many challenges to today's GPU-based HPC infrastructures. The key challenge is the huge gap between the memory requirement and its availability on GPUs. This project aims to fill this gap by developing a novel framework to reduce the memory demand effectively and efficiently via data compression technologies (e.g., lossy compression and pruning) for extreme-scale DL applications. This project is supported by NSF OAC-2034169.
2. HYLOC: AN OBJECTIVE-DRIVEN ADAPTIVE HYBRID LOSSY COMPRESSION FRAMEWORK FOR EXTREME-SCALE APPLICATIONS
Abstract: Today's extreme-scale scientific simulations and instruments are producing huge amounts of data that cannot be transmitted or stored effectively. Lossy compression, a data compression approach leading to certain data distortion, has been considered as a promising solution, because it can significantly reduce the data size while maintaining high data fidelity. However, the existing lossy compression methods may not always work effectively on all datasets used in specific applications because of their distinct and diverse characteristics. Moreover, the user objectives in compression quality and performance may vary with applications, datasets or circumstances. This project aims to develop a hybrid lossy compression framework to automatically construct the best-fit compression for diverse user objectives in data-intensive scientific research. This project is supported by NSF OAC-2003624.
3. AN EFFICIENT ERROR-BOUNDED LOSSY COMPRESSION FRAMEWORK FOR SCIENTIFIC DATA ON GPU ARCHITECTURES
Abstract: Error-bounded lossy compression is a state-of-the-art data reduction technique for HPC applications because it not only significantly reduces storage overhead but also can retain high fidelity for post-analysis. Because supercomputers and HPC applications are becoming heterogeneous using accelerator-based architectures, in particular GPUs, several development teams have recently released GPU versions of their lossy compressors. However, existing state-of-the-art GPU-based lossy compressors suffer from either low compression and decompression throughput or low compression quality. This project aims to develop an optimized GPU-based lossy compressor for scientific data. This project is supported by DOE ECP.
4. FPGA-ENHANCED SCIENTIFIC DATA MANAGEMENT
Abstract: Nowadays, many different tasks such as artificial intelligence, deep learning, graph analysis, and experimental analysis applications need to be simultaneously executed and managed along with the main simulation tasks in the supercomputer, all of which often generate huge amounts of scientific data that must be transferred for in situ processing or post analysis. To alleviate the network traffic and storage overhead, data reduction is necessarily needed by HPC in leadership computing facilities or even edge computing in experimental and observational facilities. During the past four years, SZ compression has gained much attention as a powerful data reduction technique because of its high reduction capability. However, it suffers from low throughput and high resource utilization, which impedes its adoption in many scenarios that require high-rate streaming data or use low-power embedded processors. FPGA, featuring the capabilities of configurability, high throughput, low latency, and high energy efficiency, can provide a potentially good solution to these issues. This project is to optimize and implement an FPGA-enhanced lossy compression for better scientific data management. This project is supported by Xilinx.
Please contact Dr. Tao ASAP if you're interested in Spring/Fall 2021 PhD applications and admissions. Details about more HiPDAC's research projects be found at https://www.dingwentao.com/research .
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