GTaP delivers a GPU-resident fork-join task-parallel runtime with pragma support and EPAQ that outperforms CPU OpenMP on several irregular applications.
Exploiting cxl-based memory for distributed deep learning
2 Pith papers cite this work, alongside 24 external citations. Polarity classification is still indexing.
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Reasoning workloads shift LLM inference to a capacity-bound regime where KV-cache fragmentation limits data parallelism, tensor parallelism unlocks memory at the 32B scale, and MoE models require hybrid strategies to avoid routing latency.
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GTaP: A GPU-Resident Fork-Join Task-Parallel Runtime with a Pragma-Based Interface
GTaP delivers a GPU-resident fork-join task-parallel runtime with pragma support and EPAQ that outperforms CPU OpenMP on several irregular applications.
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Understanding Inference Scaling for LLMs: Bottlenecks, Trade-offs, and Performance Principles
Reasoning workloads shift LLM inference to a capacity-bound regime where KV-cache fragmentation limits data parallelism, tensor parallelism unlocks memory at the 32B scale, and MoE models require hybrid strategies to avoid routing latency.