November 2022  [Slides]   [Poster]

Abstract: In the fields of science and engineering, lossy compression plays a growing role in running scientific simulations, as output data is on the scale of terabytes. Using error bounded lossy compression reduces the amount of storage for each simulation; however, there is no known bound for the upper limit of lossy compressibility. Data correlation structures, compressors and error bounds are factors allowing larger compression ratios and improved quality metrics. This provides one direction towards quantifying lossy compressibility. Our previous work explored 2D statistical methods to characterize the data correlation structures and their relationships, through functional models, to compression ratios and quality metrics for 2D scientific data. In this poster, we explore the expansion of our statistical methods to 3D scientific data. The method was comparable to 2D. Our work is the next step towards evaluating the theoretical limits of lossy compressibility used to predict compression performance and optimally adapt compressors.

November 2021  [Slides]   [Paper]

Abstract: Lossy compression plays a growing role in scientific simulations where the cost of storing their output data can span terabytes. Using error bounded lossy compression reduces the amount of storage for each simulation; however, there is no known bound for the upper limit on lossy compressibility. Correlation structures in the data, choice of compressor and error bound are factors allowing larger compression ratios and improved quality metrics. Analyzing these three factors provides one direction towards quantifying lossy compressibility. As a first step, we explore statistical methods to characterize the correlation structures present in the data and their relationships, through functional regression models, to compression ratios. We observed a relationship between compression ratios and several statistics summarizing the correlation structure of the data, which is a first step towards evaluating the theoretical limits of lossy compressibility used to eventually predict compression performance and adapt compressors to correlation structures present in the data.

December 2024  [Slides]

Abstract: Processing in Memory (PIM) is an accelerator emerging in many fields, offering significant potential for enhancing high-performance computing (HPC) applications. Effectively mapping an HPC application onto this architecture is challenging due to its unique instruction set, network topology, memory/cache layout, clock frequency, and programming model. In this presentation, we focus on the UPMEM PIM solution, the first general-purpose open-source PIM solution. Our primary objective is to integrate UPMEM PIM with HPC clusters by leveraging a well-vetted HPC runtime. Legion, a runtime system developed by collaborators at LANL, SLAC, NVIDIA, and Stanford University, uses data layout to schedule kernels onto a heterogeneous distributed HPC system. This presentation showcases the ongoing attempt of integrating UPMEM PIM to the backend of the Legion runtime system.

July 2023  [Slides]

Abstract: The Von-Neumann architecture has been in common-place for the last few decades. Much of the execution time on these systems is spent on memory transactions. This bottleneck can be lessened by utilizing processors in memory (PIM). Utilizing a data-centric computing model, we are expected to have speedups and reduction in energy consumption for workloads that can utilize fine grain threading. This work explores decision-support query (e.g., TPC-H) implementation on UPMEM. Future work explores targeting UPMEM using the Village toolchain.

December 2022  [Slides]

Abstract: Are you interested in graduate school as an option after graduation? Would you like to learn more about the application process, choosing your advisor, and what graduate school could mean for you? Attending graduate school in the field of Computer Science or ECE can help you stand out from others in the job market, specialize for a particular sub-field, or conduct research at the bleeding edge of your area-of-interest! If you would like to learn more and hear about other’s experiences in grad school, please attend this talk and Q&A session.

July 2022  [Slides]