arXiv subjects

Marco Kurzynski

Publications and source records attributed to Marco Kurzynski.

3 recordsLinked to original sources

arXiv2025

Chopper: A Multi-Level GPU Characterization Tool & Derived Insights Into LLM Training Inefficiency

Training large language models (LLMs) efficiently requires a deep understanding of how modern GPU systems behave under real-world distributed training workloads. While prior work has focused primarily on kernel-level performance or single-GPU microbenchmarks, the complex interaction between communication, computation, memory behavior, and power management in multi-GPU LLM training remains poorly characterized. In this work, we introduce Chopper, a profiling and analysis framework that collects, aligns, and visualizes GPU kernel traces and hardware performance counters across multiple granularities (i.e., from individual kernels to operations, layers, phases, iterations, and GPUs). Using Chopper, we perform a comprehensive end-to-end characterization of Llama 3 8B training under fully sharded data parallelism (FSDP) on an eight-GPU AMD InstinctTM MI300X node. Our analysis reveals several previously underexplored bottlenecks and behaviors, such as memory determinism enabling higher, more stable GPU and memory frequencies. We identify several sources of inefficiencies, with frequency overhead (DVFS effects) being the single largest contributor to the gap between theoretical and observed performance, exceeding the impact of MFMA utilization loss, communication/computation overlap, and kernel launch overheads. Overall, Chopper provides the first holistic, multi-granularity characterization of LLM training on AMD InstinctTM MI300X GPUs, yielding actionable insights for optimizing training frameworks, improving power-management strategies, and guiding future GPU architecture and system design.

cs.DC↗

arXiv2025

Lit Silicon: A Case Where Thermal Imbalance Couples Concurrent Execution in Multiple GPUs

GPU systems are increasingly powering modern datacenters at scale. Despite being highly performant, GPU systems can exhibit performance variation at the node and cluster levels. Such performance variation can significantly impact both high-performance computing and artificial intelligence workloads, such as cutting-edge large language models (LLMs). In this work, we analyze the performance of a single-node multi-GPU system running LLM training, and observe that the kernel-level performance variation is highly correlated with concurrent computation and communication (C3), a technique to overlap computation and communication across GPUs for performance gains. We then take a further step to reason that thermally induced straggling coupled with C3 impacts performance variation, which we coin the Lit Silicon effect. More specifically, Lit Silicon describes that in a multi-GPU node, thermal imbalance across GPUs can introduce node-level straggler GPUs (hotter and slower), which in turn slow down the leader GPUs (cooler and faster). Lit Silicon can lead to node-level performance variation and inefficiency, potentially impacting the entire datacenter. We propose analytical performance and power models for Lit Silicon, to understand the potential system-level gains. We further design simple detection and mitigation techniques to effectively address the Lit Silicon problem, and evaluate three different power management solutions, including (1) power optimization under GPU thermal design power, (2) performance optimization under node-level GPU power capping, and (3) performance optimization under node-level CPU power sloshing. We conduct experiments on two workloads on two AMD InstinctTM MI300X GPU systems under two LLM training frameworks, and observe up to 6% performance and 4% power improvements, potentially saving several tens of millions of dollars in electricity costs in datacenters.

cs.DC↗

arXiv2025

Adding MFMA Support to gem5

In this work we have enhanced gem5's GPU model support to add Matrix Core Engines (MCEs). Specifically, on the AMD MI200 and MI300 GPUs that gem5 supports, these MCEs perform Matrix Fused Multiply Add (MFMA) instructions for a variety of precisions. By adding this support, our changes enable running state-of-the-art ML workloads in gem5, as well as examining how MCE optimizations impact the behavior of future systems.

cs.AR↗