Publications (10)
TAC+: Optimizing Error-Bounded Lossy Compression for 3D AMR Simulations
Daoce Wang, Jesus Pulido, Pascal Grosset +5
Today's scientific simulations require significant data volume reduction because of the enormous amounts of data produced and the limited I/O bandwidth and storage space. Error-bou…
InferA: A Smart Assistant for Cosmological Ensemble Data
Justin Z. Tam, Pascal Grosset, Divya Banesh +3
Analyzing large-scale scientific datasets presents substantial challenges due to their sheer volume, structural complexity, and the need for specialized domain knowledge. Automatio…
LCP: Enhancing Scientific Data Management with Lossy Compression for Particles
Longtao Zhang, Ruoyu Li, Congrong Ren +10
Many scientific applications opt for particles instead of meshes as their basic primitives to model complex systems composed of billions of discrete entities. Such applications spa…
Analyzing Impact of Data Reduction Techniques on Visualization for AMR Applications Using AMReX Framework
Daoce Wang, Jesus Pulido, Pascal Grosset +3
Today's scientific simulations generate exceptionally large volumes of data, challenging the capacities of available I/O bandwidth and storage space. This necessitates a substantia…
AMRIC: A Novel In Situ Lossy Compression Framework for Efficient I/O in Adaptive Mesh Refinement Applications
Daoce Wang, Jesus Pulido, Pascal Grosset +11
As supercomputers advance towards exascale capabilities, computational intensity increases significantly, and the volume of data requiring storage and transmission experiences expo…
Understanding GPU-Based Lossy Compression for Extreme-Scale Cosmological Simulations
Sian Jin, Pascal Grosset, Christopher M. Biwer +4
To help understand our universe better, researchers and scientists currently run extreme-scale cosmology simulations on leadership supercomputers. However, such simulations can gen…
TAC: Optimizing Error-Bounded Lossy Compression for Three-Dimensional Adaptive Mesh Refinement Simulations
Daoce Wang, Jesus Pulido, Pascal Grosset +4
Today's scientific simulations require a significant reduction of data volume because of extremely large amounts of data they produce and the limited I/O bandwidth and storage spac…
STZ: A High Quality and High Speed Streaming Lossy Compression Framework for Scientific Data
Daoce Wang, Pascal Grosset, Jesus Pulido +9
Error-bounded lossy compression is one of the most efficient solutions to reduce the volume of scientific data. For lossy compression, progressive decompression and random-access d…
A High-Quality Workflow for Multi-Resolution Scientific Data Reduction and Visualization
Daoce Wang, Pascal Grosset, Jesus Pulido +8
Multi-resolution methods such as Adaptive Mesh Refinement (AMR) can enhance storage efficiency for HPC applications generating vast volumes of data. However, their applicability is…
Adaptive Configuration of In Situ Lossy Compression for Cosmology Simulations via Fine-Grained Rate-Quality Modeling
Sian Jin, Jesus Pulido, Pascal Grosset +3
Extreme-scale cosmological simulations have been widely used by today's researchers and scientists on leadership supercomputers. A new generation of error-bounded lossy compressors…