AESOP enables path-aware adversarial attacks that inflate FLOPs in ML pipelines by up to 2407x, 20x more than single-model baselines, even under defenses that force throughput collapse or data loss.
Loki: A system for serving ml inference pipelines with hardware and accuracy scaling
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LEO performs cross-vendor backward slicing from stalled GPU instructions to attribute root causes to source code, enabling optimizations that produce geometric-mean speedups of 1.73-1.82x on 21 workloads.
KEET uses LLM agents to generate data-grounded natural language explanations of performance issues in GPU kernels from Nsight Compute profiles and shows these improve downstream LLM-based optimization tasks.
citing papers explorer
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AESOP: Adversarial Execution-path Selection to Overload Deep Learning Pipelines
AESOP enables path-aware adversarial attacks that inflate FLOPs in ML pipelines by up to 2407x, 20x more than single-model baselines, even under defenses that force throughput collapse or data loss.
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LEO: Tracing GPU Stall Root Causes via Cross-Vendor Backward Slicing
LEO performs cross-vendor backward slicing from stalled GPU instructions to attribute root causes to source code, enabling optimizations that produce geometric-mean speedups of 1.73-1.82x on 21 workloads.
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KEET: Explaining Performance of GPU Kernels Using LLM Agents
KEET uses LLM agents to generate data-grounded natural language explanations of performance issues in GPU kernels from Nsight Compute profiles and shows these improve downstream LLM-based optimization tasks.