Multi-Protocol Storage for ML Workloads

Building and supporting storage systems that support both NFS (for legacy HPC workflows) and S3 (for cloud-native pipelines) over the same dataset. Key challenges include:

• Protocol semantic differences (POSIX vs object storage)
• Consistency models for concurrent access patterns
• Performance optimization for sequential reads (training) vs random access (checkpointing/Inference)

Tier-0 Caching Strategies for GPU Clusters

Researching node-local caching architectures to minimize data transfer over network fabric:

• Pre-fetching strategies based on training iteration patterns
• Cache eviction policies
• RDMA integration for sub-microsecond latency data transfers
• Benchmarking with PyTorch and TensorFlow data pipelines

Storage Benchmarking for Transformer Training

Profiling storage I/O patterns for large language model training:

• Characterizing data loading bottlenecks in transformer architectures
• Tools: FIO, elbencho, custom PyTorch profilers
• Building Load Testing Frameworks on top of Elbencho to test File and Object storage.

High-Throughput Streaming Data Ingestion and Delivery

Scaling data ingestion pipeline to billions of events and delivery across thousands of Partitions(s3-prefixes):

• Dynamic Partitioning redesign: Akka Streams architecture achieving 400% throughput improvement
• Backpressure handling and flow control in reactive systems
• Lease management for distributed coordination
• SQS/DynamoDB for workload distribution and state management

Performance Engineering

Approaches to performance optimization:

• Profiling Java services for memory leaks and GC tuning
• Native memory analysis, exploring various unix memory allocators(malloc vs JEMalloc) and heap dump investigation
• Identifying hot paths and optimizing critical sections
• Monitoring and observability for production systems

Research Interests

Current areas of exploration and experimentation:

• RDMA-enabled storage: Low-latency data access for AI workloads
• Caching hierarchies: Multi-tier caching strategies (node-local, rack-level, datacenter)
• Storage cost optimization: Balancing performance and cost for training workloads
• Benchmarking methodologies: Standardized approaches for storage performance evaluation

Technical Writing & Speaking

As an aspiring technical author, I’m interested in writing about:

• Storage architecture for AI/ML infrastructure
• Distributed systems design patterns
• Performance optimization techniques
• Real-world case studies from production systems

Interested in collaboration, co-authorship, or speaking opportunities? Reach out.