1 citations · 1 across the 4 of their papers we have counts for
5 papers
EDAN: Towards Understanding Memory Parallelism and Latency Sensitivity in HPC
Siyuan Shen, Mikhail Khalilov, Lukas Gianinazzi +6
Resource disaggregation is a promising technique for improving the efficiency of large-scale computing systems. However, this comes at the cost of increased memory access latency d…
PerfDojo: Automated ML Library Generation for Heterogeneous Architectures
Andrei Ivanov, Siyuan Shen, Gioele Gottardo +5
The increasing complexity of machine learning models and the proliferation of diverse hardware architectures (CPUs, GPUs, accelerators) make achieving optimal performance a signifi…
RailX: A Flexible, Scalable, and Low-Cost Network Architecture for Hyper-Scale LLM Training Systems
Yinxiao Feng, Tiancheng Chen, Yuchen Wei +5
Increasingly large AI workloads are calling for hyper-scale infrastructure; however, traditional interconnection network architecture is neither scalable nor cost-effective enough.…
ATLAHS: An Application-centric Network Simulator Toolchain for AI, HPC, and Distributed Storage
Siyuan Shen, Tommaso Bonato, Zhiyi Hu +3
Network simulators play a crucial role in evaluating the performance of large-scale systems. However, existing simulators rely heavily on synthetic microbenchmarks or narrowly focu…
SDR-RDMA: Software-Defined Reliability Architecture for Planetary Scale RDMA Communication
Mikhail Khalilov, Siyuan Shen, Marcin Chrapek +16
RDMA is vital for efficient distributed training across datacenters, but millisecond-scale latencies complicate the design of its reliability layer. We show that depending on long-…