Cloud HPC: How Google Scales High-Performance Computing

Cloud HPC: How Google Scales High-Performance Computing

When you think of Google Cloud, the first thing that comes to mind is usually hosting apps, databases, or analytics. But behind the scenes, Google runs some of the most advanced high-performance computing (HPC) clusters in the world. These clusters power everything from AI training to scientific simulations, using a mix of CPUs, GPUs, and custom TPUs.

In this post, we’ll break down how Google Cloud’s architecture works at scale, why different workloads need different hardware, and what it means for performance and sustainability.

From CPUs to Specialized Accelerators

The days of relying solely on CPUs are long gone. Modern cloud platforms mix different types of hardware, each optimized for different workloads:

• CPUs – General-purpose workhorses. Google’s C4 machine types scale up to 288 vCPUs and ~2.2 TB DDR5 RAM, with NVMe SSDs (up to 18 TiB) for scratch storage. They are best for request-heavy services, analytics, and orchestration tasks.
• GPUs – Built for massive data-parallelism. GCP offers A2 instances with up to 16× NVIDIA A100s (40/80 GB HBM2e) or A3 instances with H100/H200 GPUs. NVLink/NVSwitch interconnects provide intra-node bandwidth, while RoCE NICs deliver 100–400 Gbps for multi-node HPC jobs.
• TPUs – Google’s custom ASICs. A TPU v3 chip contains two 128×128 systolic arrays of MAC units plus 32 GB of on-chip HBM. At cluster scale, a TPU v4 pod has 4096 chips (~1.1 exaFLOPS bf16), while TPU v5p pods scale to 8960 chips with 95 GB each (~4.1 exaFLOPS).

This spectrum of compute nodes allows workloads to be mapped to the most suitable hardware instead of relying on a one-size-fits-all processor.

Hardware at a Glance

Node Type	Specs	Networking	Best For
CPU Node	Up to 288 vCPUs, 2.2 TB DDR5, NVMe SSD	25–100 Gbps NICs	Web services, orchestration, control plane, analytics
GPU Node	1–16 GPUs (A100/H100/H200), 40–80 GB HBM per GPU	NVLink/NVSwitch + RoCE (100–400 Gbps)	ML training, HPC simulations, CUDA/OpenCL workloads
TPU Pod	4096–8960 TPU chips (32–95 GB HBM2e each)	Custom 3D torus (4800 Gbps links) + PCIe host	Large-scale deep learning (TensorFlow/XLA)

Networking at Scale: Google’s Jupiter Fabric

Fast accelerators alone aren’t enough — interconnect bandwidth is the real bottleneck in distributed HPC.

Google’s Jupiter fabric solves this with multi-petabit per second bisection bandwidth, enabling:

• All-reduce operations for deep learning training with minimal synchronization overhead.
• MPI scaling for tightly-coupled scientific simulations (e.g., CFD, weather modeling).
• Dataflow workloads (MapReduce, Spark, Dataflow) that rely on shuffling massive datasets.

By minimizing tail latency, Jupiter allows CPUs, GPUs, and TPUs to communicate at speeds impossible with commodity Ethernet.

Trade-Offs: Throughput vs. Latency

Google’s heterogeneous design reflects workload trade-offs:

• Throughput-focused workloads (training GPT-style models or running large HPC simulations) thrive on batch parallelism with GPUs/TPUs. However, the overhead of distributing data can raise latency.
• Latency-sensitive workloads (search queries, request routing, API responses) map better to CPUs, which can respond in microseconds without batch setup.

This balance highlights the principle: use TPUs/GPUs when FLOPS/watt is the goal, CPUs when milliseconds matter.

The Energy Challenge

Pushing HPC to exascale introduces energy and cooling challenges:

• Rack density – AI racks today draw 30–100 kW each, often requiring liquid cooling.
• Cooling overhead – Cooling consumes 40–50% of data center power if unmanaged.
• Carbon footprint – Global data centers consume ~2% of electricity, projected to double by 2030.

Google mitigates this by:

• Running with an industry-leading PUE ≈ 1.10 vs. ~1.58 average.
• Making TPUs 67% more energy-efficient per chip than previous generations.
• Committing to 24/7 carbon-free energy by 2030, aligning compute growth with renewables.

This ensures scaling to exaFLOP-level compute doesn’t scale emissions at the same rate.

Cloud HPC Architecture Diagram

Since Dev.to doesn’t natively render Mermaid diagrams, here’s the architecture as an embedded image:

Why This Matters

For developers and researchers, Google Cloud’s HPC design offers:

• Faster training and simulations by matching workloads to the right hardware.
• Lower latency services thanks to CPU-optimized nodes.
• Better cost efficiency by reducing wasted compute cycles.
• A greener footprint through efficient chips and renewable energy.

Under the hood:

• Schedulers (Borg/Kubernetes) dynamically allocate jobs to CPUs, GPUs, or TPUs, ensuring utilization without starving latency-sensitive tasks.
• Memory hierarchy (DDR5 vs. GPU HBM vs. TPU HBM2e) is optimized for bandwidth, not just capacity.
• Topology-aware placement ensures data-parallel jobs run on nodes physically closer in the Jupiter network, reducing cross-rack congestion.
• Elastic scaling lets users spin up single VMs or entire TPU pods with APIs that abstract parallelism complexity.

Final Thoughts

Cloud HPC may sound like something only AI labs and scientists care about, but the reality is it underpins much of the technology we use every day.

Whether it’s smarter search results, real-time translation, or massive generative AI training runs, the infrastructure Google builds affects millions of users worldwide.

For engineers, the takeaways are clear:

• Heterogeneous compute will only grow — CPUs, GPUs, TPUs, and new accelerators (genomics, video codecs, even quantum).
• Energy-aware orchestration will be as important as performance in the coming decade.
• Networking fabrics like Jupiter redefine what’s possible at datacenter scale — bandwidth is as critical as FLOPS.
• Domain-specific hardware (like TPUs) shows that specialization beats general-purpose silicon at hyperscale.

As cloud providers race toward exascale and beyond, staying current with these architectures is key — not just for researchers but for any developer building applications that rely on scalable, efficient compute.