Research
Cache systems are core components of today’s data infrastructure. Our group focuses on designing and optimizing various types of cache systems for new hardware and infrastructure. Our goal is to improve the cache hit ratio, reduce costs, and achieve better management capabilities.
Storage and Memory systems have undergone a variety of modifications and transformations, and are widely used in today’s IT infrastructure. These systems usually have over 100 options (e.g. HBase and RocksDB) to tune performance for particular hardware (e.g., CPU, Memory, and Storage), software, and workloads (e.g., random, skewed, and read/write intensive). ASU-IDI focuses on developing an LLM-assisted auto-tuning framework for storage and memory systems to enhance performance.
Fast LLM inferencing requires sufficient GPU memory to accommodate the entire model. However, many users find low-end GPUs inadequate or even unusable for this purpose. Our focus is on developing system methodologies and solutions to speed up LLM inferencing, such as offloading, data traffic optimization, neuron and weight redistribution, and token batching and rescheduling.
LSM-KVS (e.g., RocksDB) is a key component in today’s data infrastructure for storing unstructured data. Initially designed for legacy monolithic servers, it now faces issues like resource wasting and low scalability as workloads grow. Deploying LSM-KVS in a disaggregated infrastructure is essential for better performance and resource management. Our vision is to fully restructure LSM-KVS, decouple the components, and execute them at different disaggregated compute, memory, and storage nodes for better performance, resource utilization, and management capability. Remote compaction, remote flush, and offloading the block cache are just the beginning.
In the big data era, speeding up query performance is crucial. We focus on designing novel indexing structures and storage-memory co-designs to enhance query speed, particularly in scientific computing, big data analytics, and ML systems.