Demystifying Accelerated Computing: A Deep Dive into a Critical Emerging Technology

Accelerated computing has rapidly gone from a niche technology to one of the most crucial innovations transforming business and research today. But you may still be wondering – what exactly is accelerated computing and why is it so important?

In this comprehensive guide, we‘ll demystify accelerated computing and take a deep dive into how it delivers game-changing speedups for AI, analytics, and other cutting-edge applications.

Defining Accelerated Computing

At a high level, accelerated computing refers to combining specialized hardware accelerators like GPUs with traditional CPUs to significantly improve the performance of computationally intensive workloads.

Accelerators are optimized to handle highly parallelized tasks like linear algebra or image processing. By offloading these jobs to accelerators, organizations can achieve dramatic speedups versus relying on serial CPU processing alone.

For example, Nvidia notes its latest A100 GPU provides up to 20x higher training performance compared to traditional servers. This order-of-magnitude improvement unlocks transformative new capabilities.

The Massive Growth in Model and Data Volume

Accelerated computing has become vital because of the explosive increase in both model size and data volume across industries:

  • Since 2012, the amount of data produced globally has grown from 2 zettabytes to over 59 zettabytes, a 30x increase according to Seagate.
  • The number of parameters in cutting-edge AI models like GPT-3 has ballooned from thousands to over 175 billion in the past decade, representing a 100 million x increase, as shown below:

Chart showing dramatic increase in AI model size

  • Industry research predicts the AI chipset market alone will grow from $7.3 billion in 2022 to over $200 billion by 2029, a 28x increase.

Simply put, legacy computing architectures are unable to keep pace with surging data and model complexity. Accelerated computing provides the performance boost needed to tame this exponential growth.

Diving into Key Accelerator Architectures

There are several types of hardware accelerators, each with unique strengths:

  • GPUs – Originally designed for graphics workloads, modern GPUs excel at matrix math and have become the workhorse of accelerated computing. Nvidia and AMD currently dominate the space.
  • FPGAs – Field Programmable Gate Array chips can be reconfigured on the fly and customized to accelerate specific algorithms. Intel is the primary FPGA vendor.
  • ASICs – Application Specific Integrated Circuits are hardwired for maximum performance on specialized tasks like AI inferencing but can‘t be repurposed. Startups like Groq are active here.
  • TPUs – Tensor Processing Units are Google‘s custom ASIC tailored exclusively for neural network workloads. TPUs power many Google services.

So how do these accelerators actually provide speedup versus CPUs? Here are some key architectural differences:

  • Massive parallelism – Accelerators can contain thousands of small, efficient cores enabling extreme parallelization of workloads.
  • Customized data flows – Carefully optimized on-chip data movement avoids bottlenecks and keeps cores fully fed.
  • Sparse compute – Some accelerators implement sparse compute to avoid wasting cycles on zero values common in AI models.
  • Low-precision math – Support for lower precision calculations like FP16 allows 2x higher throughput on select workloads.
  • Hardware specialization – Tailored to specific use cases, accelerators avoid general-purpose CPU overheads.

Together, these innovations allow accelerators to deliver exceptional performance on targeted computing workloads.

Accelerated Computing Powers Cutting-Edge Innovation

Accelerated computing has moved from research labs to deliver tangible business impact across industries:

  • Financial Services – Major banks use accelerated fraud detection to analyze millions of transactions to identify criminal activity in real-time.
  • Healthcare – Pharma companies like AstraZeneca harness GPUs to speed up drug discovery through molecular simulations.
  • Retail – Walmart boosted click-through rate on product recommendations by 20% using accelerated ML training.
  • Automotive – BMW improved automated driving safety by 30% using accelerated simulators to test perception models.
  • Government – The UK Met Office‘s accelerated weather prediction system provides detailed forecasts 7x faster using GPUs.

The key thread is that accelerated computing unlocks previously impossible analysis on massive datasets – from personalized medicine to hyperlocal weather prediction.

Challenges and Considerations with Accelerated Computing

While accelerators deliver game-changing speedup, there are some key challenges to consider:

  • Specialized skills – To use accelerators effectively, developers need training in languages like CUDA. Retraining represents a significant investment.
  • API complexity – There are many frameworks like CUDA, ROCm, oneAPI. Choosing and maintaining expertise in the right abstractions adds overhead.
  • Data orchestration – Shuffling vast data between accelerators, storage and other components can become a bottleneck if not designed properly.
  • Programming effort – It takes work to partition programs into discrete functions that can leverage accelerators.
  • Energy consumption – Accelerators can draw significantly more power. Data center costs need accounting.

With careful planning however, these challenges can be addressed. For instance, cloud-based accelerators allow easier experimentation before on-premise investments.

A Quick Guide to Leveraging Accelerators

Based on the rapid adoption across industries, accelerated computing is becoming a mandatory competitive capability. Here is a quick checklist to get started:

  • Take stock of bottlenecks – Map out your existing analytics and AI workflows to identify optimization opportunities.
  • Start with pilot projects – Prototype on cloud-based GPU instances before large hardware investments.
  • Evaluate different accelerators – Choose technologies like TPUs or FPGAs depending on your needs.
  • Update data pipelines – Ensure your data infrastructure supports moving large volumes to/from accelerators.
  • Provide developer training – Get teams skilled up on key languages and frameworks to maximize productivity.
  • Plan for future expansion – Storage, power, and cooling needs will evolve as accelerated workloads grow.

The Future is Accelerated

Accelerated computing has graduated from niche technology to an essential mainstream capability powering cutting-edge innovation across sectors. But we‘re just scratching the surface of what becomes possible when groundbreaking models meet flexible accelerated infrastructure.

With accelerators becoming economical and accessible, we expect to see accelerated computing enable breakthroughs from personalized healthcare to real-time autonomous driving and beyond. The future is accelerated!

I hope this guide provided useful context into the dramatic impact accelerated computing is already having on business and research. Please feel free to reach out if you have any other questions!

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