Scale out DL inference or training is no longer just a compute problem. Networking and storage optimization are becoming more critical. This is evident with a new addition to MLPerf standards with regards to storage. In this blog we briefly explore how RISC-V can help here.
The increasing complexity and data demands of deep learning (DL) models have brought new challenges to scaling out training and inference. As organizations seek to build and deploy more sophisticated AI models, the underlying infrastructure often faces bottlenecks in storage and networking, impacting both the speed and cost of large-scale AI operations.
In recent years, the RISC-V open instruction set architecture (ISA) has emerged as a promising solution to address these challenges, thanks to its customizability and flexibility.
We’ll explore the primary bottlenecks in scaling DL inference and training and how RISC-V can help alleviate these challenges.
1. Understanding Scale-Out Bottlenecks in DL Training and Inference
When scaling deep learning workloads, the main bottlenecks often arise in the areas of compute, networking traffic transmissions and storage:
- Compute: DL models are highly compute-intensive, requiring substantial parallel heterogeneous processing power. GPUs, TPUs, and specialized AI accelerators are often employed to meet these demands.
- Networking: High-speed networking transmissions is crucial for scaling, particularly when distributing models and datasets across multiple nodes. System Latency and bandwidth limitations can slow down model training and inference when data cannot move quickly enough between compute nodes and clusters.
- Storage: Large datasets must be stored and accessed at high speeds. Insufficient storage throughput can limit data availability, creating bottlenecks in data pipelines and slowing down model training and inference.
As AI datasets and model sizes grow, ensuring that compute, storage, and networking systems can keep up has become increasingly complex. For both training and inference, DL workloads can involve extensive data exchanges between storage and compute, as well as across nodes. Any lag or inefficiency in these exchanges directly impacts training times and real-time inference performance.
RISC-V can address compute, storage and networking bottlenecks in scale-out deep learning (DL) workloads through its flexible, open-source architecture that allows for tailored optimizations. Here’s how RISC-V can make a difference:
1. Custom Data Management and Storage Access
- Specialized Storage Interfaces: RISC-V enables custom hardware to handle high-throughput, low-latency storage operations, which are crucial for DL workloads that involve massive datasets. By building custom RISC-V cores for efficient data retrieval and access patterns, organizations can reduce the load on compute nodes and ensure a smoother data pipeline.
- Data Prefetching and In-Memory Computing: Custom RISC-V processors can implement advanced data prefetching strategies and in-memory processing capabilities, helping keep the compute pipelines filled. This can reduce storage latency by preloading data needed for upcoming operations, minimizing delays caused by storage read/write operations.
- Optimized Cache and Buffering Systems: By designing specialized cache and buffering hierarchies that align with the data access patterns in DL, RISC-V can improve storage throughput and reduce latency. These enhancements ensure that data needed for training or inference is available without delays.
2. Enhanced Networking for Faster Data Movement
- Network-on-Chip (NoC) Optimization: RISC-V allows custom NoC designs that facilitate low-latency communication within DL clusters. These designs can include specialized data transfer protocols and direct memory access (DMA) engines to speed up inter-core and inter-node data sharing, which is critical in distributed DL training.
- Low-Latency Communication Protocols: With RISC-V, it’s possible to implement custom communication protocols directly in hardware. For example, compression and decompression algorithms can be embedded in custom RISC-V cores to reduce the data volume sent over networks, maximizing bandwidth and reducing latency—critical for synchronizing model updates in distributed training.
- High-Throughput Data Interconnects: Custom RISC-V cores can enable high-speed networking interfaces, like RDMA (Remote Direct Memory Access), for direct memory-to-memory data transfers between nodes. RDMA is often used in high-performance computing (HPC) and can be implemented on RISC-V to speed up data movement between DL nodes without relying on traditional, slower TCP/IP stack processes.
3. Data Handling and Input/Output (I/O) Optimizations
- Parallel I/O Handling: RISC-V can be configured to efficiently handle parallel I/O requests, which helps in scenarios where multiple nodes are reading from or writing to shared storage systems. Optimizing parallel I/O can alleviate bottlenecks caused by many compute nodes trying to access the same data sources.
- Storage-Side Compute Offload: RISC-V allows for storage compute offload, where certain operations (like data preprocessing or filtering) can be executed closer to the storage layer rather than within the main compute nodes. This offloading reduces the data load on the network and allows only the most relevant data to reach the compute nodes, reducing bandwidth usage and latency.
4. Cost-Effective Scalability and Flexibility
- Low-Cost Custom Hardware: RISC-V ISA implementations offer a cost-effective approach to creating specialized hardware for storage and networking. This allows organizations to scale up their DL infrastructure affordably, with hardware specifically optimized to handle storage and networking demands.
- Adaptive and Future-Proof Infrastructure: As DL workloads evolve, RISC-V’s flexibility allows for rapid adaptation. Organizations can modify or add new instructions to handle emerging data storage and networking requirements without overhauling their hardware infrastructure.
Summary
RISC-V ISA helps manage and alleviate storage and networking bottlenecks in scale-out DL environments by:
- Enabling specialized data access and storage handling
- Facilitating low-latency, high-throughput networking for distributed DL
- Allowing efficient parallel I/O and in-storage compute offloading
- Supporting adaptable, cost-effective scaling tailored to DL demands
Through these optimizations, RISC-V provides a path to handle the massive data flow and bandwidth requirements of large-scale deep learning while minimizing delays and maintaining cost efficiency.
MIPS is leading the space providing application-specific compute cores to reduce data movement problems at scale out deployments.
