User Tools

Site Tools


start

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revisionPrevious revision
Next revision
Previous revision
start [2024/12/19 15:22] geraldodstart [2025/03/03 19:52] (current) – [Tutorial Materials] geraldod
Line 1: Line 1:
 ~~NOCACHE~~ ~~NOCACHE~~
  
-===== PPoPP 2024 Tutorial on ===== +===== PPoPP 2025 Tutorial on ===== 
 ===== Memory-Centric Computing Systems (Half Day) ===== ===== Memory-Centric Computing Systems (Half Day) =====
  
Line 13: Line 13:
 Recent PIM products and prototypes place compute units near the memory arrays. New memory interfaces like CXL (Compute Express Link) aid the enablement of compute-capable memories. At the same time, academia and industry are actively exploring other types of PIM by, e.g., exploiting the analog operation of DRAM, SRAM, flash memory, and emerging non-volatile memories, and hybrid PIM architectures that combine processing capabilities of different types and at different parts of the memory/storage hierarchy.  Recent PIM products and prototypes place compute units near the memory arrays. New memory interfaces like CXL (Compute Express Link) aid the enablement of compute-capable memories. At the same time, academia and industry are actively exploring other types of PIM by, e.g., exploiting the analog operation of DRAM, SRAM, flash memory, and emerging non-volatile memories, and hybrid PIM architectures that combine processing capabilities of different types and at different parts of the memory/storage hierarchy. 
    
-{{:memory_centric_comp_ppopp_banner.jpg?400 |}}+{{:memory_centric_comp_ppopp_banner.jpeg?400 |}}
  
 PIM can improve performance and energy efficiency for many modern applications, enabling a commercially viable way of dealing with huge amounts of data bottlenecking our computing systems, which is especially exacerbated by workloads like AI/ML and genomics. In fact, workloads like large language model training and inference can potentially be “killer applications'' for PIM.  PIM can improve performance and energy efficiency for many modern applications, enabling a commercially viable way of dealing with huge amounts of data bottlenecking our computing systems, which is especially exacerbated by workloads like AI/ML and genomics. In fact, workloads like large language model training and inference can potentially be “killer applications'' for PIM. 
Line 21: Line 21:
 This tutorial focuses on the latest advances in PIM technology, spanning both hardware and software, including novel PIM ideas, different tools and frameworks to conduct PIM research, and programming techniques and optimization strategies for  PIM kernels. We will (1) provide an introduction to PIM and the taxonomy of PIM systems, (2) give an overview and a rigorous analysis of existing PIM hardware from industry and academia, (3) provide and describe hardware and software infrastructures that can enable new and experienced researchers to conduct research in PIM systems, and (4) shed light on how to improve future PIM systems for emerging memory-bound workloads. The tutorial will also incorporate invited talks from leading industry and academic researchers in PIM systems.  This tutorial focuses on the latest advances in PIM technology, spanning both hardware and software, including novel PIM ideas, different tools and frameworks to conduct PIM research, and programming techniques and optimization strategies for  PIM kernels. We will (1) provide an introduction to PIM and the taxonomy of PIM systems, (2) give an overview and a rigorous analysis of existing PIM hardware from industry and academia, (3) provide and describe hardware and software infrastructures that can enable new and experienced researchers to conduct research in PIM systems, and (4) shed light on how to improve future PIM systems for emerging memory-bound workloads. The tutorial will also incorporate invited talks from leading industry and academic researchers in PIM systems. 
  
 +**Location:** [[https://ppopp25.sigplan.org/room/PPoPP-2025-venue-mesquite-5| Mesquite 5 ]] 
  
 ==== Livestream ==== ==== Livestream ====
-[[https://www.youtube.com/live/Eo7WSDJ1084?feature=shared|YouTube livestream]] +[[https://www.youtube.com/live/NkDY6osus6g?feature=shared|YouTube livestream]] 
-{{youtube>Eo7WSDJ1084?large}}+{{youtube>NkDY6osus6g?large}} 
 + 
  
 ==== Organizers ==== ==== Organizers ====
Line 48: Line 51:
 ==== Tutorial Materials ==== ==== Tutorial Materials ====
  
-TBA+ 
 +^ Time ^ Speaker ^ Title ^ Materials ^ 
 +| 08:00am   | Geraldo F. Oliveira | Logistics |{{geraldo-ppopp25-lecture0-introduction-beforelecture.pdf|(PDF)}} {{geraldo-ppopp25-lecture0-introduction-beforelecture.pptx|(PPT)}}| 
 +| 08:00am-08:30am   | Prof. Onur Mutlu | Recent Advances in Processing-in-DRAM |{{onur-MCC-PPoPP-MemoryCentricComputing-1-March-2025.pdf|(PDF)}} {{onur-MCC-PPoPP-MemoryCentricComputing-1-March-2025.pptx|(PPT)}}| 
 +| 08:30am-09:30am   | Geraldo F. Oliveira | Processing-Near-Memory Systems: Developments from Academia & Industry | {{geraldo-ppopp25-lecture2-processing-near-memory-afterlecture.pdf|(PDF)}} {{geraldo-ppopp25-lecture2-processing-near-memory-afterlecture.pptx|(PPT)}}| 
 +| 09:30am-10:00am   | Geraldo F. Oliveira | Programming Processing-Near-Memory Systems |{{geraldo-ppopp25-lecture3-adoption-programmability-afterlecture.pdf|(PDF)}} {{geraldo-ppopp25-lecture3-adoption-programmability-afterlecture.pptx|(PPT)}}| 
 +| 10:00am-10:30am   | N/A | Coffee Break | | 
 +| 10:30am-11:00am   | Geraldo F. Oliveira | Processing-Using-Memory Systems for Bulk Bitwise Operations | {{geraldo-ppopp25-lecture4-processing-using-memory-afterlecture.pdf|(PDF)}} {{geraldo-ppopp25-lecture4-processing-using-memory-afterlecture.pptx|(PPT)}}| 
 +| 11:00am-11:30am   | Ataberk Olgun | Infrastructure for Processing-Using-Memory Research | {{ataberk-ppopp25-lecture5-pim-infrastructure-beforelecture.pdf|(PDF)}} {{ataberk-ppopp25-lecture5-pim-infrastructure-beforelecture.pptx|(PPT)}}| 
 +| 11:30am-12:00pm  | [[https://icn.kaist.ac.kr/~jjk12/|Prof. John Kim]] | Is it Memory-Centric or Communication-Centric? | {{MemoryCentric_Tutorial_2025_JohnKim.pdf|(PDF)}} |
  
 ==== Invited Speakers ==== ==== Invited Speakers ====
  
-TBA+ 
 +=== Prof. John Kim ===   
 +**Talk Title:** Is it Memory-Centric or Communication-Centric?{{ ::jkim_headshot.jpg?nolink&200|}} 
 + 
 +**Talk Abstract:** Memory-centric computing is crucial for overcoming data bottlenecks in modern computing by bringing processing closer to memory, reducing latency, and improving energy efficiency. One obvious example of memory-centric computing is Processing-in-Memory (PIM).   However, as memory-centric becomes more attractive, this talk argues how communication-centric needs to be prioritized even within memory-centric systems.  I will talk about recent work on how data movement needs to be minimized and optimized within PIM systems.  
 + 
 +**Bio:** [[https://icn.kaist.ac.kr/~jjk12/index.html|Prof. John Kim]] John Kim is currently a full professor in the School of Electrical Engineering at KAIST (Korea Advanced Institute of Science and Technology) in Daejeon, Korea. John Kim received his Ph.D. from Stanford University and B.S/M.Eng from Cornell University. His research interests include computer architecture, interconnection networks, security, and mobile systems.  He has received a Google Faculty Research Award and Microsoft-Asia New Faculty Fellowship/. He is listed in the Hall of Fame for ISCA, MICRO, and HPCA and is an IEEE Fellow. 
 + 
  
 ==== Learning Materials ==== ==== Learning Materials ====
start.1734621774.txt.gz · Last modified: 2024/12/19 15:22 by geraldod

Donate Powered by PHP Valid HTML5 Valid CSS Driven by DokuWiki