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1. Composable Infrastructures for an Academic Research Environment: Lessons Learned

   https://doi.org/10.1109/IPDPSW55747.2022.00208  

   Long, Lance; Bargo, Timothy; Renambot, Luc; Brown, Maxine; Johnson, Andrew E. (May 2022, 36th IEEE International Parallel & Distributed Processing Symposium Workshops (IPDPSW))

   Composable infrastructure holds the promise of accelerating the pace of academic research and discovery by enabling researchers to tailor the resources of a machine (e.g., GPUs, storage, NICs), on-demand, to address application needs. We were first introduced to composable infrastructure in 2018, and at the same time, there was growing demand among our College of Engineering faculty for GPU systems for data science, artificial intelligence / machine learning / deep learning, and visualization. Many purchased their own individual desktop or deskside systems, a few pursued more costly cloud and HPC solutions, and others looked to the College or campus computer center for GPU resources which, at the time, were scarce. After surveying the diverse needs of our faculty and studying product offerings by a few nascent startups in the composable infrastructure sector, we applied for and received a grant from the National Science Foundation in November 2019 to purchase a mid-scale system, configured to our specifications, for use by faculty and students for research and research training. This paper describes our composable infrastructure solution and implementation for our academic community. Given how modern workflows are progressively moving to containers and cloud frameworks (using Kubernetes) and to programming notebooks (primarily Jupyter), both for ease of use and for ensuring reproducible experiments, we initially adapted these tools for our system. We have since made it simpler to use our system, and now provide our users with a public facing JupyterHub server. We also added an expansion chassis to our system to enable composable co-location, which is a shared central architecture in which our researchers can insert and integrate specialized resources (GPUs, accelerators, networking cards, etc.) needed for their research. In February 2020, installation of our system was finalized and made operational and we began providing access to faculty in the College of Engineering. Now, two years later, it is used by over 40 faculty and students plus some external collaborators for research and research training. Their use cases and experiences are briefly described in this paper. Composable infrastructure has proven to be a useful computational system for workload variability, uneven applications, and modern workflows in academic environments. 

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2. COMPaaS DLV: Composable Infrastructure for Deep Learning in an Academic Research Environment

   https://doi.org/10.1109/ICNP.2019.8888070  

   Brown, Maxine; Renambot, Luc; Long, Lance; Bargo, Timothy; Johnson, Andrew E. (October 2019, 2019 IEEE 27th International Conference on Network Protocols (ICNP))

   In today's Big Data era, data scientists require new computational instruments in order to quickly analyze large-scale datasets using complex codes and quicken the rate of scientific progress. While Federally-funded computer resources, from supercomputers to clouds, are beneficial, they are often limiting - particularly for deep learning and visualization - as they have few Graphics Processing Units (GPUs). GPUs are at the center of modern high-performance computing and artificial intelligence, efficiently performing mathematical operations that can be massively parallelized, speeding up codes used for deep learning, visualization and image processing, more so than general-purpose microprocessors, or Central Processing Units (CPUs). The University of Illinois at Chicago is acquiring a much-in-demand GPU-based instrument, COMPaaS DLV - COMposable Platform as a Service Instrument for Deep Learning & Visualization, based on composable infrastructure, an advanced architecture that disaggregates the underlying compute, storage, and network resources for scaling needs, but operates as a single cohesive infrastructure for management and workload purposes. We are experimenting with a small system and learning a great deal about composability, and we believe COMPaaS DLV users will benefit from the varied workflow that composable infrastructure allows. 

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3. Moving from Composable to Programmable

   https://doi.org/10.1109/IPDPSW55747.2022.00209  

   Chen, Zhongyi; Renambot, Luc; Long, Lance; Brown, Maxine; Johnson, Andrew E. (May 2022, 36th IEEE International Parallel & Distributed Processing Symposium Workshops (IPDPSW))

   In today’s Big Data era, data scientists require modern workflows to quickly analyze large-scale datasets using complex codes to maintain the rate of scientific progress. These scientists often rely on available campus resources or off-the-shelf computational systems for their applications. Unified infrastructure or over-provisioned servers can quickly become bottlenecks for specific tasks, wasting time and resources. Composable infrastructure helps solve these problems by providing users with new ways to increase resource utilization. Composable infrastructure disaggregates a computer’s components – CPU, GPU (accelerators), storage and networking – into fluid pools of resources, but typically relies upon infrastructure engineers to architect individual machines. Infrastructure is either managed with specialized command-line utilities, user interfaces or specification files. These management models are cumbersome and difficult to incorporate into data-science workflows. We developed a high-level software API, Composastructure, which, when integrated into modern workflows, can be used by infrastructure engineers as well as data scientists to reorganize composable resources on demand. Composastructure enables infrastructures to be programmable, secure, persistent and reproducible. Our API composes machines, frees resources, supports multi-rack operations, and includes a Python module for Jupyter Notebooks. 

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4. Composable Rate-Independent Computation in Continuous Chemical Reaction Networks

   https://doi.org/10.1007/978-3-319-99429-1_15  

   Chalk, C; Kornerup, N; Reeves, W; Soloveichik, D (January 2018, Computational Methods in Systems Biology. CMSB 2018.)

   Biological regulatory networks depend upon chemical interactions to process information. Engineering such molecular computing systems is a major challenge for synthetic biology and related fields. The chemical reaction network (CRN) model idealizes chemical interactions, abstracting away specifics of the molecular implementation, and allowing rigorous reasoning about the computational power of chemical kinetics. Here we focus on function computation with CRNs, where we think of the initial concentrations of some species as the input and the eventual steady-state concentration of another species as the output. Specifically, we are concerned with CRNs that are rate-independent (the computation must be correct independent of the reaction rate law) and composable (𝑓∘𝑔 can be computed by concatenating the CRNs computing f and g). Rate independence and composability are important engineering desiderata, permitting implementations that violate mass-action kinetics, or even “well-mixedness”, and allowing the systematic construction of complex computation via modular design. We show that to construct composable rate-independent CRNs, it is necessary and sufficient to ensure that the output species of a module is not a reactant in any reaction within the module. We then exactly characterize the functions computable by such CRNs as superadditive, positive-continuous, and piecewise rational linear. Our results show that composability severely limits rate-independent computation unless more sophisticated input/output encodings are used. 

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5. Digital Light Processing Based Bioprinting with Composable Gradients

   https://doi.org/10.1002/adma.202107038  

   Wang, Mian; Li, Wanlu; Mille, Luis_S; Ching, Terry; Luo, Zeyu; Tang, Guosheng; Garciamendez, Carlos_Ezio; Lesha, Ami; Hashimoto, Michinao; Zhang, Yu_Shrike (October 2021, Advanced Materials)

   Abstract Recapitulation of complex tissues signifies a remarkable challenge and, to date, only a few approaches have emerged that can efficiently reconstruct necessary gradients in 3D constructs. This is true even though mimicry of these gradients is of great importance to establish the functionality of engineered tissues and devices. Here, a composable‐gradient Digital Light Processing (DLP)‐based (bio)printing system is developed, utilizing the unprecedented integration of a microfluidic mixer for the generation of either continual or discrete gradients of desired (bio)inks in real time. Notably, the precisely controlled gradients are composable on‐the‐fly by facilely by adjusting the (bio)ink flow ratios. In addition, this setup is designed in such a way that (bio)ink waste is minimized when exchanging the gradient (bio)inks, further enhancing this time‐ and (bio)ink‐saving strategy. Various planar and 3D structures exhibiting continual gradients of materials, of cell densities, of growth factor concentrations, of hydrogel stiffness, and of porosities in horizontal and/or vertical direction, are exemplified. The composable fabrication of multifunctional gradients strongly supports the potential of the unique bioprinting system in numerous biomedical applications. 

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