EDBT 2026 Demo / reviewers in the wild / expert
Hanna Cha
dblp:230/2602
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3ranked-venue papers
1as first author
3since 2021 · last 2025
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 3 · 1 first-author · 3 since 2021Software engineering, systems software and programming languages · 1 · 1 first-author · 1 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | GCStack+GCScaler: Fast and Accurate GPU Performance Analyses Using Fine-Grained Stall Cycle Accounting and Interval AnalysisabstractTo design next-generation Graphics Processing Units (GPUs), GPU architects rely on GPU performance analyses to identify key GPU performance bottlenecks and explore GPU design spaces.Unfortunately, the existing GPU performance analysis mechanisms make it difficult for GPU architects to conduct fast and accurate GPU performance analyses.The existing mechanisms can provide misleading Hanna Cha, Sungchul Lee, Jounghoo Lee, Yeonan Ha, Joonsung Kim 0001, Youngsok Kim |
ISCA | 1 |
| 2025 | LATPC: Accelerating GPU Address Translation Using Locality-Aware TLB Prefetching and MSHR Compression
Yeonan Ha, Hanna Cha, Jiwon Lee 0001, Joonsung Kim 0001, Won Woo Ro, Youngsok Kim |
MICRO | 3 |
| 2024 | MPC-Wrapper: Fully Harnessing the Potential of Samsung Aquabolt-XL HBM2-PIM on FPGAsabstractProcessing-In-Memory (PIM) is an attractive solution for mitigating frequent and large data movement between computational units and memory devices. Among various PIM implementations, Samsung Aquabolt-XL is an HBM2 memory device which implements 16 PIM-enabled pseudo-channels and associates an In-Memory Processor (IMP) to each pair of the memory banks. Recent studies have shown that Aquabolt-XL can greatly accelerate various applications (e.g., deep learning) by offloading memory-intensive operations (e.g., matrix-vector multiplications) to the IMPs. However, the prior study fails to fully utilize Aquabolt-XL and achieves limited performance gains by offloading operations to the IMPs of only a single pseudo-channel. Ideally, utilizing all the 16 pseudo-channels of Aquabolt-XL can further accelerate the key operations by a factor of 16× compared to utilizing only a single pseudo-channel. To fully exploit Aquabolt-XL, therefore, memory-intensive operations should be offloaded to and concurrently executed on the IMPs of all the PIM-enabled pseudo-channels. This paper presents MPC-Wrapper, a multi-pseudo-channel wrapper interface which allows memory-intensive operations to be offloaded to and concurrently executed on the IMPs of all the 16 PIM-enabled pseudo-channels of Aquabolt-XL. First, MPC-Wrapper allows all the PIM-enabled pseudo-channels to operate independently and in parallel, thus achieving high scalability needed for fully utilizing all the PIM-enabled pseudo-channels of Aquabolt-XL. Second, MPC-Wrapper is highly flexible as it exposes the PIM-enabled pseudo-channels as separate ports and enables an FPGA logic to flexibly utilize any set of the PIM-enabled pseudo-channels according to its needs. Third, MPC-Wrapper achieves high usability by hiding the complex low-level interactions between the memory controller and Aquabolt-XL for initializing and invoking the PIM-enabled pseudo-channels from the other FPGA logics. Using an Aquabolt-XL-equipped Xilinx Alveo U280 FPGA and four memory-intensive benchmarks, we show that utilizing all the 16 PIM-enabled pseudo-channels of Aquabolt-XL with MPC-Wrapper achieves a geometric mean speedup of 13.66× over the baseline single PIM-enabled pseudo-channel implementations of the benchmarks. Jinwoo Choi 0003, Yeonan Ha, Hanna Cha, Seil Lee, Sungchul Lee, Jounghoo Lee, Shinhaeng Kang, Bongjun Kim, Hanwoong Jung, Hanjun Kim 0001, Youngsok Kim |
FCCM | 3 |