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2. The infrastructure powering IBM's Gen AI model development

Author

Gershon, Talia, Seelam, Seetharami, Belgodere, Brian, Bonilla, Milton, Hoang, Lan, Barnett, Danny, Chung, I-Hsin, Mohan, Apoorve, Chen, Ming-Hung, Luo, Lixiang, Walkup, Robert, Evangelinos, Constantinos, Salaria, Shweta, Dombrowa, Marc, Park, Yoonho, Kayi, Apo, Schour, Liran, Alim, Alim, Sydney, Ali, Maniotis, Pavlos, Schares, Laurent, Metzler, Bernard, Karacali-Akyamac, Bengi, Wen, Sophia, Chiba, Tatsuhiro, Choochotkaew, Sunyanan, Yoshimura, Takeshi, Misale, Claudia, Elengikal, Tonia, Connor, Kevin O, Liu, Zhuoran, Molina, Richard, Schneidenbach, Lars, Caden, James, Laibinis, Christopher, Fonseca, Carlos, Tarasov, Vasily, Sundararaman, Swaminathan, Schmuck, Frank, Guthridge, Scott, Cohn, Jeremy, Eshel, Marc, Muench, Paul, Liu, Runyu, Pointer, William, Wyskida, Drew, Krull, Bob, Rose, Ray, Wolfe, Brent, Cornejo, William, Walter, John, Malone, Colm, Perucci, Clifford, Franco, Frank, Hinds, Nigel, Calio, Bob, Druyan, Pavel, Kilduff, Robert, Kienle, John, McStay, Connor, Figueroa, Andrew, Connolly, Matthew, Fost, Edie, Roma, Gina, Fonseca, Jake, Levy, Ido, Payne, Michele, Schenkel, Ryan, Malki, Amir, Schneider, Lion, Narkhede, Aniruddha, Moshref, Shekeba, Kisin, Alexandra, Dodin, Olga, Rippon, Bill, Wrieth, Henry, Ganci, John, Colino, Johnny, Habeger-Rose, Donna, Pandey, Rakesh, Gidh, Aditya, Gaur, Aditya, Patterson, Dennis, Salmani, Samsuddin, Varma, Rambilas, Rumana, Rumana, Sharma, Shubham, Mishra, Mayank, Panda, Rameswar, Prasad, Aditya, Stallone, Matt, Zhang, Gaoyuan, Shen, Yikang, Cox, David, Puri, Ruchir, Agrawal, Dakshi, Thorstensen, Drew, Belog, Joel, Tang, Brent, Gupta, Saurabh Kumar, Biswas, Amitabha, Maheshwari, Anup, Gampel, Eran, Van Patten, Jason, Runion, Matthew, Kaki, Sai, Bogin, Yigal, Reitz, Brian, Pritko, Steve, Najam, Shahan, Nambala, Surya, Chirra, Radhika, Welp, Rick, DiMitri, Frank, Telles, Felipe, Arvelo, Amilcar, Chu, King, Seminaro, Ed, Schram, Andrew, Eickhoff, Felix, Hanson, William, Mckeever, Eric, Light, Michael, Joseph, Dinakaran, Chaudhary, Piyush, Shivam, Piyush, Chaudhary, Puneet, Jones, Wesley, Guthrie, Robert, Bostic, Chris, Islam, Rezaul, Duersch, Steve, Sawdon, Wayne, Lewars, John, Klos, Matthew, Spriggs, Michael, McMillan, Bill, Gao, George, Kamra, Ashish, Singh, Gaurav, Curry, Marc, Katarki, Tushar, Talerico, Joe, Shi, Zenghui, Malleni, Sai Sindhur, and Gallen, Erwan

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Artificial Intelligence
Abstract

AI Infrastructure plays a key role in the speed and cost-competitiveness of developing and deploying advanced AI models. The current demand for powerful AI infrastructure for model training is driven by the emergence of generative AI and foundational models, where on occasion thousands of GPUs must cooperate on a single training job for the model to be trained in a reasonable time. Delivering efficient and high-performing AI training requires an end-to-end solution that combines hardware, software and holistic telemetry to cater for multiple types of AI workloads. In this report, we describe IBM's hybrid cloud infrastructure that powers our generative AI model development. This infrastructure includes (1) Vela: an AI-optimized supercomputing capability directly integrated into the IBM Cloud, delivering scalable, dynamic, multi-tenant and geographically distributed infrastructure for large-scale model training and other AI workflow steps and (2) Blue Vela: a large-scale, purpose-built, on-premises hosting environment that is optimized to support our largest and most ambitious AI model training tasks. Vela provides IBM with the dual benefit of high performance for internal use along with the flexibility to adapt to an evolving commercial landscape. Blue Vela provides us with the benefits of rapid development of our largest and most ambitious models, as well as future-proofing against the evolving model landscape in the industry. Taken together, they provide IBM with the ability to rapidly innovate in the development of both AI models and commercial offerings., Comment: Corresponding Authors: Talia Gershon, Seetharami Seelam,Brian Belgodere, Milton Bonilla

Published
2024

17. Resident Care in the "In-Between Time": Cross-Sector Perspectives on Enhancing Synergy Between Palliative Care and Permanent Supportive Housing.

Author

Johnson, Ian M., Traver, Anthony C., and Light, Michael A.

Subjects
HOMELESS persons, HOUSING policy, PALLIATIVE treatment, INTEGRATED health care delivery, THEMATIC analysis
Abstract

Despite established disparities in serious illness prevalence among formerly homeless people living in permanent supportive housing, there is a dearth of research aimed at promoting and enhancing palliative care service delivery in this setting. This paper describes current practices, perceived barriers, and recommendations in permanent supportive housing for residents living with serious illness through a thematic analysis of interviews with cross-sector stakeholders (n = 30) from the Research and Supportive Care at Later-life for Unhoused People (RASCAL-UP) study. Themes identified across resident care, program collaboration, and policy and environment areas included: (1) unique resident care needs; (2) on-site relationships; (3) adaptation of aging, disability, and healthcare services for residents; (4) internal implementation barriers; (5) preparing the environment for serious illness; and (6) missing levels of care. From these results, we put forth recommendations for future collaborative care intervention development, supportive housing policy, and capacity-building efforts for healthcare and housing providers alike. [ABSTRACT FROM AUTHOR]

Published
2024
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18. Citizenship, legal status, and misdemeanor justice.

Author

Light, Michael T., Robey, Jason P., and Kim, Jungmyung

Subjects
*UNDOCUMENTED immigrants, *INDICTMENTS, *STATUS (Law), *PUNISHMENT, *NONCITIZENS, *MISDEMEANORS
Abstract

Although minor forms of criminal justice contact are increasingly used to identify immigration violators, little research has been conducted at the intersection of immigration and misdemeanor justice. As a result, citizenship remains undertheorized in punishment research and fundamental questions remain unanswered. In this article, we introduce the "crimmigrant" punishment framework to conceptualize the unique case processing consequences for non–U.S. citizens and undocumented immigrants. We then draw on rich case data from all misdemeanor arrests in Texas and California between 2006 and 2018 to establish four notable findings. (1) Misdemeanors are common and consequential. We observe more than 1.4 million misdemeanor arrests involving non–U.S. citizens, the overwhelming majority of which resulted in criminal charges and formal punishments. (2) The offenses that funnel noncitizens into the misdemeanor system are similar to those of U.S. citizens; however, we do observe an appreciable number of arrests linked to noncitizens' legal status (e.g., giving false information). (3) Once in the misdemeanor system, noncitizens, and especially undocumented immigrants, are significantly more likely to be convicted and incarcerated compared with similarly situated U.S. citizens. (4) These disparities are more severe in Texas than in California. [ABSTRACT FROM AUTHOR]

Published
2024
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19. Did Immigrant Arrest Rates Change During the Trump Administration? Evidence From California and Texas.

Author

Light, Michael T., Boisten, Laura, and Kim, Jungmyung

Subjects
*IMMIGRATION enforcement, *IMMIGRATION status, *PRESIDENTIAL administrations, *ARREST, *CRIME, *TRAFFIC violations
Abstract

Despite substantial legal and policy debates, whether immigrant arrest rates changed during the Trump presidency remains surprisingly understudied. This is partially because immigration status is rarely available in crime data. We address this gap by applying difference-in-differences (DD) and difference-in-difference-in-differences (DDD) estimations to detailed arrest data from Texas and California from 2015 to 2018. We find little evidence, descriptive or otherwise, to suggest that the transition from the Obama to the Trump administration had a meaningful impact on immigrant arrests, whether measured as violence, property, drug, or traffic offenses. These results suggest that the immigration enforcement initiatives under President Trump did not deliver on their crime reduction pledges, but they also provide little evidence of over-policing of immigrants in discretionary actions such as traffic arrests. [ABSTRACT FROM AUTHOR]

Published
2024
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27. Increased homicide played a key role in driving Black-White disparities in life expectancy among men during the COVID-19 pandemic.

Author

Light, Michael T. and Vachuska, Karl

Subjects
*COVID-19 pandemic, *LIFE expectancy, *WHITE men, *AFRICAN Americans, *BLACK men
Abstract

Disparities in life expectancy between Black and White Americans increased substantially during the COVID-19 pandemic. During the same period, the US experienced the largest increase in homicide on record. Yet, little research has examined the contribution of homicide to Black-White disparities in longevity in recent years. Using mortality data and population estimates, we conduct a comprehensive decomposition of the drivers of Black-White inequality in life expectancy and lifespan variability between 2019 and 2021 among men. We find that homicide is one of the principal reasons why lifespans have become shorter for Black men than White men in recent years. In 2020 and 2021, homicide was the leading contributor to inequality in both life expectancy and lifespan variability between Black and White men, accounting for far more of the racial gap in longevity and variability than deaths from COVID-19. Addressing homicides should be at the forefront of any public health discussion aimed at promoting racial health equity. [ABSTRACT FROM AUTHOR]

Published
2024
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30. [Policy Brief]

Author

Light, Michael T. and Marshall, Joey

Published
2018

40. The Pet Paradox: Uncovering the Role of Animal Companions During the Serious Health Events of People Experiencing Homelessness.

Author

Ward, Chesney, Johnson, Ian, Bamwine, Patricia, and Light, Michael

Subjects
PETS, HOMELESS persons, HOMELESS children, HUMAN-animal relationships, PARADOX, CHRONIC diseases, ANIMAL owners
Abstract

Despite the prevalence of animal companions in the lives of homeless individuals and the established benefits and complications offered by animals during serious illness and end-of-life (EOL), little is known about the experiences of homeless people who have animal companions and chronic illness. Two subsamples from a larger study – longitudinal medical documentation of homeless palliative-care patients who discussed animal companionship during their care (n = 11) and interviews with cross-sector service providers (n = 3) who spoke to the role of animals in care – were analyzed using an interpretive description approach. Results showcased three major themes: (1) healing benefits to animal companionship during concurrent homelessness and serious illness; (2) choosing animal companionship over formal housing and health services; (3) when animal companionship's consequences outweigh the benefits. These findings provide practical implications for clinical assessment, programing, and service expansion for persons with animal companions and future translational science to benefit animal companion owners experiencing serious illnesses while homeless. [ABSTRACT FROM AUTHOR]

Published
2024
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47. Forced Expiratory Volume in 1 Second Variability Helps Identify Patients with Cystic Fibrosis at Risk of Greater Loss of Lung Function

Author

Morgan, Wayne, Konstan, Michael, Liou, Ted, McColley, Susanna, McMullen, Ann, Quittner, Alexandra, Regelmann, Warren, Ren, Clement, Rosenfeld, Margaret, Sawicki, Greg, Schechter, Michael, VanDevanter, Donald, Wagener, Jeffrey, Woo, Marlyn, Brasfield, Dana, Lyrene, Raymond, Sindel, Lawrence, Roberts, Dion, Carroll, John, Warren, Robert, Nassri, Louay, Anderson, Paula, Brown, Mark, Silverthorn, Amy, Radford, Peggy, Gong, Gerald, Legris, Gregory, Greene, Gerald, Sudhakar, Reddivalam, Platzker, Arnold, Nickerson, Bruce, Hardy, Karen, Harwood, Ivan, Shay, Gregory, Quick, Bryon, Lieberthal, Allan, Moss, Richard, Landon, Chris, Fanous, Yvonne, Lieberman, Jay, Spiritus, Eugene, Chipps, Bradley, McDonald, Ruth, Pian, Mark, Cropp, Gerd, Lewis, Nancy, Nielson, Dennis, Shapiro, Bertrand, Wagener, Jeff, Accurso, Frank, Saavedra, Milene, Daigle, Karen, Hen, Jacob, Palazzo, Regina, Dodds, Kathryn, Pad-man, Raj, Goodill, John, Winnie, Glenna, Davies, Lea, Kriseman, Tony, Sallent, Jorge, Chiaro, Joseph, Kubiet, Martin, Goldfinger, Sue, Schwartzman, Morton, Diaz, Carlosenrique, Maupin, Kevin, Riff, Eduardo, Geller, David, Livingston, Floyd, Mavunda, Kunjana, Birriel, Jose, Jr., Faverio, Luis, Rosenberg, David, Schaeffer, David, Sherman, James, Wagner, Mary, Light, Michael, Schnapf, Bruce, Montgomery, Gary, Kirchner, Kevin, Weatherly, Mark, Caplan, Daniel, Guill, Margaret, Hudson, Valera, Akhter, Javeed, Davison, Donald, Boas, Steven, Chung, Youngran, Latner, Rennee, Aljadeff, Gabriel, Chan, Youngran, Kraut, Jerome, Stone, Arvey, Still, John Lloyd, Sharma, Girish, Eagleton, Lanie, Hopkins, Patricia, Chatrath, Umesh, Lester, Lucille, Kim, Young-Jee, Anthony, Veena, Eigan, Howard, Howenstine, Michelle, James, Pushpom, Gergesha, Edward, Harris, James, Plant, Robert, Zivkovich, Veljko, Collins, Angela, Nassif, Edward, Ahrens, Richard, Doornbos, Daniel, Kanarek, Joseph, Leff, Richard, Shaw, Pamela, Demoss, Elanor, Riva, Maria, Sullivan, Leonard, Anstead, Michael, Kanga, Jamshed, Eid, Nemr, Morton, Ron, Hilman, Bettina, Jones, Kim, Davis, Scott, Harder, Ralph, Lever, Tom, Cairns, Anne Marie, Caldwell, Edgar, Zuckerman, Jonathan, Mogayzel, Peter, Rosenstein, Beryl, McQuestion, John, Perry, Donna, Rosenberg, Samuel, Gerstle, Robert, Colin, Andrew, Wohl, Mary Ellen, Lapey, Allan, Yee, William, O'Sullivan, Brian, Zwerdling, Robert, Abdulhamid, Ibrahim, O'Hagan, Adrian, Schuen, John, Kurlandsky, Lawrence, Honicky, Richard, Homnick, Douglas, Marks, John, Pichurko, Bohdan, Maxvold, Norma, Nasr, Samya, Simon, Richard, Tsai, Wan, Kissner, Dana, McNamara, John, Henry, Nancy, Marker, Stephen, Pryor, Michael, Walker, Lynn, Woodward, Jim, Mizell, Louis, Miller, Suzanne, Rosenbluth, Daniel, Black, Philip, McCubbin, Michael, Cohen, Alan, Ferkol, Thomas, Mallory, George, Rejent, Anthony, Rubin, Bruce, Graff, Gavin, Konig, Peter, Colombo, John, Murphy, Peter, Boyle, William, Parker, Worth, Patton, Chandler, Zanni, Robert, Atlas, Arthur, Turcios, Nelson, Laraya-Cuasay, Lourdes, Bisberg, Dorothy, Aguila, Helen, Allen, Sarah, James, David, Perkett, Elizabeth, Thompson, Marsha, Budhecha, Sonia, Diaz, Ruben, Rosen, Jonathan, Kaslovsky, Robert, Percciacante, Ronald, Borowitz, Drucy, Cronin, Joseph, McMahon, Colin, Quittell, Lynne, Giusti, Robert, Cohen, Rubin, DeCelie-Germana, Joan, Gorvoy, Jack, Patel, Kalpan, Kattan, Meyer, Dozor, Allen, DiMango, Emily, Berdella, Maria, Anbar, Ran, Ianuzzi, Debra, Sexton, James, Tayag-Kier, Catherine, McBride, John, Voter, Karen, Dimaio, Mary, Georgitis, John, Majure, Joseph Marc, Martinez, Maria, McIntosh, Clarke, Leigh, Margaret, Black, Hugh, Hughes, James, Kantak, Anand, Wilmott, Robert, Omlor, Gregory, Stone, Robert, McCoy, Karen, Acton, James, Doershuk, Carl, Fink, Robert, Steffan, Michael, Vauthy, Pierre, Joseph, Patricia, Reyes, Santiago, Kramer, John, Royall, James, Eisenberg, Jay, Wall, Michael, Fiel, Stanley, Scanlin, Thomas, Phadke, Shroti, Weinberg, Joel, Sexauer, William, Wolf, Stephen, Holsclaw, Douglas, Klein, Debra, Warren, Stuart, Kinsey, Robert, Perez, Carlos, Ganeshanathan, Muttiah, Shinnick, James, Panitch, Howard, Varlotta, Laurie, Robinson, Cynthia, Santana, Jose Rodriguez, Passero, Mary Ann, Gwinn, Jane, Baker, Robert, Bowman, Michael, Flume, Patrick, Brown, Daniel, Marville, Roxanne, Wallace, James, Parry, Rodney, Ellenburg, Don, Rogers, John, Mohon, Ricky, Ledbetter, Joel, Hanissian, Aram, Schoumacher, Robert, Campbell, Preston, Harris, Christopher, Slovis, Bonnie, Stokes, Dennis, Hale, Kathryn, Katz, Marcia, Seilheimer, Dan, Sockrider, Marianne, Frank, Allan, Daniel, James, Cunningham, James, Browning, Iley, Bray, John, Dove, Amanda, Mandujano, Fernando, Tremper, Larry, Morse, Martha, Willey-Courand, Donna, Copenhaver, Steven, Pohl, John, McWilliams, Bennie, Martine-Logvinoff, Marie, Wallace, Marsh, Klein, Robert, Amaro, Rodolfo, Couch, Leslie, Brown, Michael, Prestidge, Claude, Inscore, Stephen, Lipton, Andrew, Chatfield, Barbara, Liou, Theodore, Marshall, Bruce, Lahiri, Thomas, Swartz, Donald, Whittaker, Laurie, Karlson, Karl, Ropoll, Ignacio, Rubio, Thomas, Schmidt, Joel, Thomas, David, Osborn, John, Froh, Deborah, Gaston, Benjamin, Elliott, Greg, Gibson, Ronald, Ramsey, Bonnie, McCarthy, Michael, Larson, Lawrence, Ricker, David, Robbins, Mark, Aitken, Moira, Emerson, Julia, Aronoff, Stephen, Moffett, Kathryn, Biller, Julie, Splaingard, Mark, Sullivan, Bradley, Pritchard, Paul, Adair, Stu, Holzwarth, Peter, Dopico, Guillermo, Meyer, Keith, Green, Christopher, Rock, Michael, Morgan, Wayne J., VanDevanter, Donald R., Pasta, David J., Foreman, Aimee J., Wagener, Jeffrey S., and Konstan, Michael W.

Published
2016
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48. Meaningful Healthcare and Social Service Access for Homeless Populations: Generating Alliances Through Theories of Therapeutic Landscape.

Author

Johnson, Ian M. and Light, Michael A.

Subjects
*MEDICAL personnel, *SOCIAL services, *PATIENT-centered care, *PALLIATIVE treatment, *HOMELESS persons, *MEDICAL care, *HOMELESSNESS
Abstract

Poor health can both facilitate and be facilitated by homelessness. The desire for serious illness care to occur at home and increased attention to home-and-community-based health services prompts consideration of how the therapeutic nature of place can shape patient-centered care for those who live outside of traditional housing. Three key concepts from therapeutic landscape are illustrated by findings from an empirical, grounded theory study (RASCAL-UP) of a mobile palliative care provider for the homeless: (1) healing pilgrimages and migrations; (2) place-based therapeutic networks; (3) spaces of care and control. Through application of therapeutic landscape theory, health workers can better comprehend the healthcare decisions and goals of people experiencing homelessness. Applications of therapeutic landscape theory can guide healthcare professionals toward equitable care planning and support programming and policy to enhance care options for homeless populations. [ABSTRACT FROM AUTHOR]

Published
2024
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