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December 01, 2023

Intraoperative 2D/3D Image Registration via Differentiable X-ray Rendering

Vivek Gopalakrishnan
CSAIL MIT
4:00P
- 4:30P

Location

32-G882 (Hewlett Room)
Add to Calendar 2023-12-01 16:00:00 2023-12-01 16:30:00 America/New_York Intraoperative 2D/3D Image Registration via Differentiable X-ray Rendering Abstract: We investigate the camera pose estimation problem in the context of 2D/3D medical image registration. The application is to align 2D intraoperative images (e.g., X-ray) to a patient's 3D preoperative volume (e.g., CT), helping provide 3D image guidance during minimally invasive surgeries. We present a patient-specific self-supervised approach that uses differentiable rendering to achieve the sub-millimeter accuracy required in this context. Some of aspects of our work that may be of interest to the broader ML community include- How do you exactly compute the rendering equation for differentiable ray tracing through a voxel grid?- What is the optimal representation of rotations and translations when using gradient descent to optimize poses?- What is the optimal image loss function that achieves robust image registration while still being fast enough to use in real time?Speaker bio: Vivek is a 3rd year PhD student in Polina Golland's group broadly interested in 3D computer vision problems across science and medicine. 32-G882 (Hewlett Room)

December 05, 2023

High-Performance GPU Code Generation for Mining Motifs in Temporal Graphs

Nishil Talati
University of Michigan
2:00P
- 3:00P

Location

32-D463
32-D463
Add to Calendar 2023-12-05 14:00:00 2023-12-05 15:00:00 America/New_York High-Performance GPU Code Generation for Mining Motifs in Temporal Graphs This is hybrid meeting. The physical room is 32-D463 (Star). The Zoom registration link is https://mit.zoom.us/meeting/register/tJMtdOqgrTwsGNV_k0Nk6JjLMk-G6GFzTRyk.******************IMPORTANT NOTE ABOUT ONLINE REGISTRATION******************- The registration link for 2023 is the same as the link from 2022.- Please save the Zoom link that you receive after you register. This link will stay the same for all subsequent Fast Code seminars.- Zoom does not recognize a second registration, and will not send out the link a second time. The organizers will not be notified of any second registration.- If you have any problems with registration, please contact lindalynch@csail.mit.edu by 12pm on the day of the seminar, so that we can try to resolve it before the seminar begins.*********************************************************************Abstract:Temporal motif mining is the task of finding the occurrences of subgraph patterns withina large input temporal graph that obey the specified structural and temporal constraints.Despite its utility in several critical application domains that demand high performance(e.g., detecting fraud in financial transaction graphs), the performance of existing softwareis limited on commercial hard- ware platforms, in that it runs for tens of hours. In this talk,I will present Everest - a system that efficiently maps the workload of mining (supportsboth enumeration and counting) temporal motifs to the highly parallel GPU architecture.Using input temporal graph and a more expressive user-defined temporal motif querydefinition, Everest generates an execution plan and runtime primitives that optimize theworkload execution by exploiting the high compute throughput of a GPU. Everestgenerates motif-specific mining code to reduce long-latency memory accesses andfrequent thread divergence operations. Everest incorporates novel low-cost runtimemechanisms to enable load balancing to improve GPU hardware utilization. To supportlarge graphs that do not fit on GPU memory, Everest also supports multi-GPU executionby intelligently partitioning the edge list that prevents inter-GPU communication. Everesthides the implementation complexity of presented optimizations away from the targetedsystem user for better usability. Our evaluation shows that, using proposed optimizations,Everest improves the performance of a baseline GPU implementation by 19x, on average.Speaker Bio:Nishil Talati is an Assistant Research Scientist (Research Faculty) at the CSE departmentof University of Michigan. He earned his PhD from University of Michigan. Nishil’sresearch interests include computer architecture and systems software design forimproving the performance of modern data-intensive workloads. His research is publishedat several top-tier venues including ISCA, MICRO, HPCA, ASPLOS, and others. Nishil’swork has been recognized as the 2021 HPCA best paper award, 2023 DATE best paperhonorable mention, and 2023 IISWC best paper nominee.Relevant Publication:Y. Yuan, H. Ye, S. Vedula, W. Kaza, and N. Talati, "Everest: GPU-Accelerated System for Mining TemporalMotifs," at International Conference on Very Large Databases (VLDB 2024). 32-D463

December 06, 2023

Cloud-Native Database with Storage Disaggregation

Xiangyao Yu
University of Wisconsin - Madison
1:00P
- 2:00P

Location

32-G575
Add to Calendar 2023-12-06 13:00:00 2023-12-06 14:00:00 America/New_York Cloud-Native Database with Storage Disaggregation itle=====Cloud-Native Database with Storage DisaggregationAbstract========Modern databases are moving to the cloud for high elasticity and flexible pricing model (e.g., pay-as-you-go). Cloud-native databases adopt a unique storage-disaggregation architecture, where the computation and storage are decoupled as two separate services connected through the data center network. The new storage-disaggregation architecture demands a revisit of database system design. In this talk, I will discuss our research in optimized disaggregated database systems in both transactional and analytical workloads. In particular, I will present Cornus, an optimized two-phase commit (2PC) protocol that leverages highly-available storage services to reduce latency and enhance reliability for distributed transactions. I will also discuss our recent work on computation pushdown, where the database leverages storage-layer computation (i.e., a pushdown layer) to accelerate data analytics processing. I will conclude the talk with our on-going research and future plans on cloud-native database systems.Short Bio=========Xiangyao Yu is an Assistant Professor at the University of Wisconsin-Madison. His research interests include (1) cloud-native databases, (2) new hardware for databases, and (3) transactions and HTAP. Before joining UW-Madison, he finished postdoc and PhD at MIT and bachelor at Tsinghua University.Zoom option:Tianyu Li is inviting you to a scheduled Zoom meeting.Join Zoom Meetinghttps://mit.zoom.us/j/92698683855?pwd=NWhsVjdVblRvd3g5U0p3VDRIcDJTQT09Password: 329527One tap mobile+16465588656,,92698683855# US (New York)+16699006833,,92698683855# US (San Jose)Meeting ID: 926 9868 3855US : +1 646 558 8656 or +1 669 900 6833International Numbers: https://mit.zoom.us/u/acWjKZOLZiJoin by SIP92698683855@zoomcrc.comJoin by Skype for Businesshttps://mit.zoom.us/skype/92698683855 32-G575