Understanding What AI Workloads Are and How to Tackle Their Challenges

What is AI Workload?

AI workloads are the computational tasks of AI systems. They include model training, inference, and optimization, and GPUs, TPUs, or FPGAs help process huge datasets and algorithms. They automate monotonous tasks, decision-making, and intractable issues while revamping industries. In manufacturing, predictive maintenance analyzes real-time equipment data. 

In healthcare, AI-driven diagnoses beat humans. Notably, generative AI's promise drives 40% of companies to spend more. AI workloads improve efficiency via parallelization and distributed computing, scalability in data-intensive applications, and dynamic resource optimization.

Types of AI Workloads

Machine Learning (ML)

AI workloads in machine learning refer to the iterative optimization of algorithms over structured datasets to predict or classify data. E.g., Random Forest and XGBoost might be used for tabular data due to their handling of missing values and complex interactions. Note that ML training pipelines use distributed frameworks, including Apache Spark, to process petabyte-scale datasets for latency to milliseconds. GPUs or TPUs are less critical in traditional ML unless datasets are above terabytes or tasks involve hyperparameter tuning across feature sets.

Deep Learning (DL)

Deep learning AI workloads necessitate parallel computation for models such as transformers or CNNs. For example, training a GPT-like model with 175 billion parameters demands clusters of A100 GPUs operating with 500+ TFLOPS per node. Apart from that, distributed deep learning frameworks, including Horovod, optimize such workloads while scaling across GPUs with linear efficiency. Dataset preprocessing for DL in the exabyte range is likewise important. It exploits TensorFlow Extended (TFX) for automated pipeline management.

Natural Language Processing (NLP)

NLP workloads utilize transformer-based models like BERT and T5. They handle multi-language corpora exceeding terabytes. Such models need memory-optimized GPUs to process sequences of tokens. For example, real-time NLP inference for question answering demands latency under 100 ms. It is attainable with edge-optimized hardware. What is more, fine-tuning LLMs on domain-specific data, including medical journals, implicates adapters like LoRA to decrease memory overhead compared to traditional methods.

Computer Vision

Computer vision AI workloads depend on CNNs and vision transformers for image segmentation and object detection. For example, training models on ImageNet (14+ million images) may demand high-throughput GPU arrays. Sparse convolution layers lower FLOP requirements in 3D vision tasks, including LiDAR-based mapping. Meanwhile, real-time facial recognition at 60+ FPS can use specialized edge accelerators like Intel Movidius for energy-efficient processing.

Challenges Associated with AI Workloads

Data Complexity

AI workloads use unstructured data like text, images, and videos. Managing such data demands powerful ETL processes and feature engineering pipelines. For instance, large language models can process datasets above 570 GB during pre-training. Hence, it renders cleaning and deduplication critical. Apache Hadoop and Dask might handle distributed data transformation. Nevertheless, guaranteeing real-time ETL for streaming data adds complexity.

Performance

AI workloads are compute-intensive. They demand low latency and high throughput. Training models entail billions of parameters and can consume hundreds of teraflops per second for weeks. Systems need optimized batch processing and caching to avoid bottlenecks. For example, GPUs with HBM2 memory cut data transfer delays for performance in video recognition.

Infrastructure

AI workloads demand unique hardware and elasticity. Training LLMs may require clusters of GPUs interconnected with InfiniBand and Ethernet for lower latency. On-premises setups combine GPUs and CPUs for dense computing. Alternatively, cloud-based solutions offer scalability but need optimized orchestration Kubernetes to avoid underutilization or over-provisioning.

Processing Power

AI workloads require hardware acceleration beyond CPUs. More than 10,000 of the minimum required 4 billion GPUs can operate in parallel. ASICs, including Google's Edge TPU, deliver 2 TOPS inferences per watt for edge AI for energy-efficient deployments. Remember, TPUs are appropriate for TensorFlow-specific tasks for massive matrix multiplications.

Scalability

AI workloads grow with data and models. For instance, training may involve billions of parameters, scaling beyond traditional systems. Elastic object storage like Amazon S3 handles petabytes of data to back this growth. Parallelization frameworks distribute training across GPUs for lower convergence times from weeks to days, even with billion-parameter models.

Optimizing AI Workloads: Networking Considerations

High Bandwidth and Low Latency

Distributed Training Requirements:
In distributed AI models, rapid data transmission between GPUs or TPUs is essential to maintain synchronization and minimize training time.

Solutions:

• InfiniBand and Ultra Ethernet: InfiniBand provides ultra-low latency and high throughput, making it ideal for AI workloads requiring high-frequency data exchanges. Ultra Ethernet is emerging as a strong contender in high-performance computing (HPC) and AI contexts, supporting data transfer speeds beyond800 Gbps.
• RDMA (Remote Direct Memory Access): RDMA enables direct memory-to-memory data transfers between devices without involving the CPU, significantly reducing communication latency and overhead.

Scalable Network Architecture

Solutions:

• Optical Interconnects: These are crucial for large-scale data centers, capable of handling the massive traffic surges brought by scaling AI models.
• Fabric Technology: NVIDIA's NVLink and NVSwitch offer high-speed connections that facilitate efficient communication between nodes, improving scalability for AI workloads. 

Dynamic Resource Allocation

Solutions:

• Quality of Service (QoS): QoS prioritizes critical tasks, such as real-time inference, ensuring efficient and reliable data transmission.

Bottlenecks in Data Transmission

Solutions:

• Hierarchical Storage Architecture: Combining caching systems like Redis with object storage solutions like Amazon S3 reduces latency between main memory and storage devices, enabling faster data access.
• Partitioned Data Transfer: Tools like Apache Kafka and Apache Hadoop alleviate I/O bottlenecks in high-throughput scenarios by efficiently managing data distribution and processing.

Looking Ahead: Optimizing Networking for AI Workloads

Advanced, scalable solutions that meet AI application needs help improve networking for AI workloads. UfiSpace offers high-performance networking equipment for such demands. Our S9321-64E 800G data center switch handles huge, low-entropy AI/ML workloads with adaptive routing and end-to-end congestion management. Our DDC solution integrates the S9725-64E 800G Disaggregated Fabric Router and the S9720-56ED Line Card Router, working together to provide scalable, non-blocking backplane switch fabric for AI cluster interconnects.

Using these solutions, organizations can build a solid, efficient, and low-latency AI infrastructure. To keep up with AI networking, try these products.