Studies of information representation practices have shown that knowledge productions are conditioned by the affordances of those representing practices/systems like information architectures, classification schemas, and database management systems (Bowker&Star 2000; Bowker 2005; Poirier 2017) and that those systems’ affordances are materialized, carrying their own weight despite information’s supposed immateriality/virtuality (Edwards, 2010; Chun 2011; Dourish 2017; Jones 2017; Ribes 2017). Nevertheless, data processing practices having been parallelized and scaled out for last decades, from Google’s MapReduce, Spanner, BigTable (enabling to replace oligopolistic vendors’ proprietary software/hardware solutions with open-sourced/commodified software/hardware combinations), through Apache Hadoop (modeling Google’s distributed software), to Transformer (a more parallelizable deep-learning model, on which OpenAI’s GPT-3 was built) to name a few, have been less scrutinized, though these distributed practices contributed much to solving scalability issues (c.f. Ribes 2014) in ever-increasing resource-heavy computing systems, being epistemically infrastructured (Malazita 2019).
    This panel seeks to collectively problematize those distributed systems for storing, indexing, retrieving, and pattern-recognizing data, and explore the themes/methods including,-but-not-limited-to,
– How have distributed/parallel computing architectures (either software-or-hardware, or both) and algorithmic/programming models appeared in relation to others (Stevens 2013; Dourish 2016)? How are materialities and scalabilities related there?
– Which hegemonic cultural/societal norms have those parallel systems and then-contemporary societies had in common (McPherson 2012)?
– Are there any homogenizing, mono-cultural/geographical, or proprietary conditions these distributed computing machineries demand? By which methods would those conditions be noticed? What consequences follow? if those conditions should be cancelled/loosened, which alternatives (e.g. computing “of” the South) be needed (Amrute&Murillo 2020; Takhteyev 2012; Ochigame 2020)? 
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REFERENCES
– Amrute, Sareeta, and Luis Felipe R. Murillo. “Introduction: Computing in/from the South.” Catalyst: Feminism, Theory, Technoscience 6, no. 2 (2020): 1-23.
– Bowker, Geoffrey C. Memory Practices in the Sciences. Cambridge, MA: MIT Press, 2005.
– Bowker, Geoffrey C., and Susan Leigh Star. Sorting Things Out: Classification and its consequences. Cambridge, MA: MIT Press, 2000.
– Chun, Wendy Hui Kyong. “Always already there, or software as memory,” in Programmed Visions: Software and Memory, 137-173. Cambridge, MA: MIT Press, 2011.
– Dourish, Paul. “Algorithms and their others: Algorithmic culture in context.” Big Data & Society 3, no. 2 (2016): 1-11.
– Dourish, Paul. The Stuff of Bits: An essay on the materialities of information. Cambridge, MA: MIT Press, 2017.
– Edwards, Paul N. A Vast Machine: Computer models, climate data, and the politics of global warming. Cambridge, MA: MIT Press, 2010.
– Jones, Matthew L. “Querying the Archive: Data Mining from Apriori to PageRank.” In Science in the Archives: Pasts, Presents, Futures, edited by Lorraine Daston, 311-328. Chicago, IL: University of Chicago Press, 2017.
– Malazita, James W., and Korryn Resetar. “Infrastructures of abstraction: how computer science education produces anti-political subjects.” Digital Creativity 30, no. 4 (2019): 300-312.
– McPherson, Tara. “US Operating Systems at Mid-Century: The Intertwining of Race and UNIX.” In Race after the Internet, edited by Lisa Nakamura, Peter Chow-White, 21-37. New York, NY: Routledge, 2012.
– Ochigame, Rodrigo. “Informatics of the oppressed.” LOGIC 11 (2020): 53-74.
– Poirier, Lindsay. “A Turn for the Scruffy: An Ethnographic Study of Semantic Web Architecture.” In Proceedings of the 2017 ACM on Web Science Conference, pp. 359-367. 2017.
– Ribes, David. “Ethnography of scaling, or, how to a fit a national research infrastructure in the room.” In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing, pp. 158-170. 2014.
– Stevens, Hallam. Life out of sequence: a data-driven history of bioinformatics. University of Chicago Press, 2013.