Paper Podcast - Secret behind SambaNova superfast LLM Inferencing Speed
The memory wall refers to the growing disparity between processor speed and memory access speed in computer systems. As processors have become faster, memory access times have not improved at the same rate, creating a bottleneck in system performance.
Novel SN40L architecture combines streaming dataflow and tiered memory to efficiently. This enables efficient deployment of trillion-parameter Composition of Experts systems with significant performance gains.
📚 https://arxiv.org/pdf/2405.07518
Original Problem 🔍:
Monolithic LLMs are expensive to train and deploy. Composition of Experts (CoE) with smaller specialized models is more cost-effective but faces challenges in efficient execution on conventional hardware.
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Solution in this Paper 🛠️:
• SN40L Reconfigurable Dataflow Unit (RDU):
- Streaming dataflow architecture with Pattern Compute Units (PCUs), Pattern Memory Units (PMUs), and Address Generation and Coalescing Units (AGCUs)
- Three-tier memory system: on-chip SRAM, HBM, DDR DRAM
- Flexible address generation and data alignment units
- Hardware support for peer-to-peer communication
• Samba-CoE: 150-expert system deployed on SN40L Node (8 RDU sockets)
• Software stack for efficient memory management across DDR and HBM
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Key Insights from this Paper 💡:
• Streaming dataflow enables aggressive operator fusion beyond conventional techniques
• Three-tier memory system crucial for efficient CoE execution
• Hardware-orchestrated kernel launches significantly reduce overheads for autoregressive decoding
• Efficient memory management and dynamic expert switching are key to CoE performance
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Results 📊:
• Streaming dataflow provides 2×-13× speedup over unfused baselines
• SN40L Node vs. DGX systems for Samba-CoE:
- 19× reduction in machine footprint
- 15×-31× faster model switching
- 3.7×-6.6× overall speedup
• Demonstrates feasibility of trillion-parameter CoE deployment on a single node
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