High-Performance Computing for Big Data

9 Powerful 𝗦𝗽𝗮𝗿𝗸 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 𝗶𝗻 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 (With Real Examples) One of our ETL pipelines used to take 10 hours to complete. After tuning and scaling in Databricks, 𝘄𝗲 𝗯𝗿𝗼𝘂𝗴𝗵𝘁 𝗶𝘁 𝗱𝗼𝘄𝗻 𝘁𝗼 ~𝟭 𝗵𝗼𝘂𝗿 — 𝗮 𝟵𝟬% 𝗿𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗶𝗻 𝗿𝘂𝗻𝘁𝗶𝗺𝗲. That’s the real impact of Spark optimization. Databricks (built on Apache #Spark) is incredibly powerful for big data, ML, and real-time analytics — but without the right optimizations, jobs can quickly become slow, expensive, and difficult to scale. In this post, I’m sharing 9 proven techniques that helped us achieve 5×–10× speedups on production pipelines (handling hundreds of millions of rows and datasets up to 500TB):  1. Cluster & Resource Optimization – Autoscaling, Photon, and right-sizing clusters  2. Smart Partitioning – Reduce unnecessary scans and improve parallelism  3. Caching & Persistence – Avoid recomputation for iterative workloads  4. Efficient File Formats – Delta/Parquet + ZSTD for faster I/O  5. Delta Lake Optimization – OPTIMIZE, Z-ORDER, VACUUM  6. Broadcast Joins – Eliminate expensive shuffles  7. Skew Handling – Fix uneven data distribution  8. Structured Streaming – Low-latency pipelines with Auto Loader  9. Adaptive Query Execution (#AQE) – Smarter runtime optimizations 𝗜𝗺𝗽𝗮𝗰𝘁: ✔️ 5×–10× faster pipelines ✔️ 30%+ reduction in cloud costs ✔️ More scalable and reliable data workflows Whether you’re working on #ETL pipelines, Machine Learning, or real-time analytics — these optimizations can make a massive difference. 👉 For us, Autoscaling + Photon + Delta optimization were game changers. #Databricks #ApacheSpark #BigData #DataEngineering #DataPipeline #Analytics #PerformanceOptimization #DataArchitecture #DeltaLake #SparkOptimization #BigDataEngineering

The GPUs were top-tier. The models were solid. Training was still slow. The real problem? The data pipeline feeding them. GPU performance is rarely limited by compute alone. It’s limited by how efficiently data moves, loads, and synchronizes. Here’s the structured 10-step path 👇 Step 1: Define Target GPU Throughput Start by calculating samples per second per GPU and defining a minimum sustained throughput target. Design for steady performance, not peak spikes. Step 2: Co-Locate Compute and Data Keep data physically close to GPUs to reduce cross-rack traffic, latency variability, and east-west congestion that silently kills scaling. Step 3: Implement Multi-Level Caching Use layered caching - object storage, distributed cache, node-local SSD, and memory buffers - to keep GPUs continuously fed. Cold storage should never directly serve GPUs. Step 4: Parallelize Data Loading Increase data loader workers, enable asynchronous prefetching, and overlap I/O with compute. If GPUs wait for data, your scaling breaks. Step 5: Design for Distributed Synchronization Align shard distribution across training nodes, avoid duplicate reads, and balance partitions evenly to prevent gradient sync delays and network spikes. Step 6: Select the Right Storage Architecture Evaluate object storage for durability, distributed file systems for throughput, and NVMe for hot data. Hybrid storage layers outperform single-tier designs. Step 7: Optimize Data Format and Serialization Adopt columnar formats like Parquet, compress intelligently, and reduce decoding overhead. Inefficient serialization wastes more compute than expected. Step 8: Minimize CPU Bottlenecks Monitor CPU saturation, optimize preprocessing, and remove heavy Python loops. GPUs depend on CPUs to prepare data efficiently. Step 9: Map the Data Access Pattern Analyze sequential vs random reads, shuffle frequency, augmentation intensity, and batch size. Most inefficiencies come from misunderstood access patterns. Step 10: Monitor and Continuously Benchmark Track GPU utilization, data loader wait time, and end-to-end samples per second. You cannot optimize what you don’t measure. The core principle: Throughput > Theoretical FLOPS. AI performance is a pipeline problem, not just a hardware problem. If your GPUs aren’t hitting expected utilization, the bottleneck is probably upstream.

High Level HPC Cluster Design: As we move into the world of ML and GPGPU programming, data centers filled with GPUs are becoming critical infrastructure for these workloads. However, all data centers are not equal for all workloads. High Performance Compute (HPC) clusters should be designed with your specific workload(s) in mind. So many factors need to be considered when designing a cluster relative to your use case: - What compute capabilities are needed? - Is super fast storage needed? - How should the nodes be positioned relative to each other? - Do we need ultra-fast internode connectivity? Diving into 3 use cases, we can begin to think about some of these scenarios. - Large Scale Distributed Training - Production Inference - Large Scale Genomics I picked these because they have significantly different characteristics. Large Scale Distributed Training characteristics: - Increase learning rate as a function of batch size - More networked nodes == more throughput - Not real time - Possible to save states between epochs Production Inference: - Handle thousands of simultaneous requests - Often real time - user waiting on answer - High availability, uptime, robust - Running same model on all nodes - low internode communication Large Scale Genomics: - High disk utilization - Potential node-to-node communication, but not mandatory. GPUs within same node - Not real time, restartable but cannot checkpoint Given these significantly different use cases, critical resources may be different in each cluster. While each probably wants the fastest GPUs possible, the rest of the cluster may utilize different features. Large Scale Distributed Training requires mandatory high speed internode communication for faster training. This means high speed cables and well designed networking. However, due to checkpointing resources may be saved on node redundancy. Production Inference stays within the same node, but nodes __must__ be available when a user makes a request. This means resources would be better spent providing redundancy than high speed internode networking. Lastly, Genomics leverages a lot of IO. Slow disks can be the difference between a job going from hours to minutes. The fastest disks available can make a huge difference here, while some resources can be spared on internode communication and redundancy. With these examples, I'm trying to highlight a pattern. All use cases are not the same, and we don't always have the maximum available resources. When considering trade-offs, consider which design is most appropriate for your use case. This will give you the biggest bang for your buck when deciding on how to design your cluster. This situation is not specific to owning your cluster either. This is relevant for both on-prem and cloud based workflows. Everything counts, but usually a few things are the most critical. If you like my content, feel free to follow or connect! #softwareengineering #hpc

⚡ 𝗛𝗼𝘄 𝗦𝗽𝗮𝗿𝗸 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀 𝟱𝗧𝗕 𝗼𝗳 𝗗𝗮𝘁𝗮 - 𝗕𝗲𝗵𝗶𝗻𝗱 𝘁𝗵𝗲 𝗦𝗰𝗲𝗻𝗲𝘀 Ever wondered what really happens when you submit a Spark job on 5TB? Here’s the simplified breakdown 👇 🔹 Input Splitting 5TB (5000 GB) gets split into ~128MB chunks → ~40,000 partitions. 🔹 Cluster Setup Example: 10 nodes × 8 cores = 80 cores → 80 partitions processed in parallel per wave. 🔹 Execution Waves (~500 Rounds) Tasks are scheduled across executors and CPU cores. Processing happens in waves until all partitions are complete. 🔹 Processing Engine • SQL execution • Joins • Shuffles • Aggregations All driven by memory + disk I/O. 🔹 File Format Matters Parquet / Delta → Columnar, compressed, faster CSV / JSON → Higher memory usage, slower scans Partition pruning (date/region) reduces scan size. 🔹 Join Optimization • Broadcast small tables (<10MB) • Bucket large tables • Partition on join keys • Tune shuffle partitions 🔴 Common Bottlenecks • Too few cores → slow waves • Too many partitions → overhead • Data skew → executor hotspots • Excessive shuffle → network I/O spike 📊 Watch Spark UI for: Tasks | Memory | Shuffle | Skew Big data performance isn’t magic. It’s partition math + resource balance + smart file design. #ApacheSpark #BigData #SparkOptimization #DataEngineering #Databricks #PerformanceTuning #Lakehouse #ModernDataStack

Interviewer:_ Let's say we're processing a massive dataset of 5 TB in Databricks. How would you configure the cluster to achieve optimal performance? _Candidate:_ To process 5 TB of data efficiently, I'd recommend a cluster configuration with a mix of high-performance nodes and optimized storage. First, I'd estimate the number of partitions required to process the data in parallel. Assuming a partition size of 256 MB, we'd need: 5 TB = 5 x 1024 GB = 5,120 GB Number of partitions = 5,120 GB / 256 MB = 20,000 partitions To process these partitions in parallel, we need to determine the optimal number of nodes. A common rule of thumb is to allocate 1-2 CPU cores per partition. Based on this, we can estimate the total number of CPU cores required: 20,000 partitions x 1-2 CPU cores/partition = 20,000-40,000 CPU cores Assuming each node has 200-400 partitions/node (a reasonable number to ensure efficient processing), we can estimate the number of nodes required: Number of nodes = Total number of partitions / Partitions per node = 20,000 partitions / 200-400 partitions/node = 50-100 nodes In terms of memory, we need to ensure that each node has sufficient memory to process the partitions. A common rule of thumb is to allocate 2-4 GB of memory per CPU core. Based on this, we can estimate the total memory required: 50-100 nodes x 20-40 GB/node = 1000-4000 GB Therefore, we'd recommend a cluster configuration with: - 50-100 high-performance nodes (e.g., AWS c5.2xlarge or Azure D16s_v3) - 20-40 GB of memory per node This configuration would provide a good balance between processing power and memory capacity. _Interviewer:_ That's a great approach! How would you decide the number of executors and executor cores required? _Candidate:_ To decide the number of executors and executor cores, I'd consider the following factors: - Number of partitions: 20,000 partitions - Desired level of parallelism: 50-100 nodes - Memory requirements: 20-40 GB per node Assuming 5-10 executor cores per node, we'd need: Number of executor cores = 50-100 nodes x 5-10 cores/node = 250-1000 cores Number of executors = Number of executor cores / 5-10 cores/executor = 25-100 executors _Interviewer:_ What about memory requirements? How would you estimate the total memory required? _Candidate:_ To estimate the total memory required, I'd consider the following factors: - Number of executors: 25-100 executors - Memory per executor: 20-40 GB Total memory required = Number of executors x Memory per executor = 500-4000 GB Therefore, we'd need a cluster with at least 500-4000 GB of memory to process 5 TB of data efficiently. _Interviewer:_ Finally, can you tell me how you'd handle data skew and optimize data processing performance? To handle data skew, I'd use techniques like: - Salting: adding a random value to the partition key to reduce skew - Bucketing: dividing data into smaller buckets to reduce skew #DataEngineer #BigData #DElveWithVani #PySpark #Spark #SQL