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Workload Transformation and Innovations in POWER Architecture
- 1. Workload Transformation & Innovationin POWER Architecture 16th July 2021 IIT Roorkee – Invited Talk Satish Kumar Sadasivam satsadas@in.ibm.com Senior Performance Architect, Master Inventor IBM Systems
- 3. Agenda l New Eraof IT - Pillars of change l Workload Transformation l Building blocks of Compute Infrastructure l AI & Hardware l Introduction to POWER ISA & MMA l POWER10 & its AI acceleration capabilities
- 4. New Era ofIT is defined and driven by AI & Cloud AI helps enrich the user experience Cloud defines/drives the next generation software design & development architecture and deployment Infrastructure
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- 8. AI as aservice Lot of cloud companies are adapting AI as a service solution through their cloud offering • IBM Watson Cloud • Amazon Web Services (AWS) • Microsoft Azure • Google Cloud • Advantages • Advanced infrastructure with minimal cost • Pay for What you Use • Ease of Usage • Options for Scalability • Disadvantages • Reduced Security • Increasing reliance on Third-Parties • Long-Term Costs • Reduced Transparency https://www.researchgate.net/figure/AI-Platform-Architecture-21_fig7_332555792 https://www.geeksforgeeks.org/what-is-artificial-intelligence-as-a-service-aiaas-in-the-tech-industry/
- 9. Enterprise Database architecture Domain: •Three Tier Architecture Characteristics: • Highly Data centric with less compute demand. • System Performance dominated by throughput and bandwidth. Applications & Languages: • C,C++ and JAVA • Applications were highly monolithic • Traditional SQL databases & structured data. Domain: • Cloud Native Architecture • Infusion of compute centric AI algorithms into the enterprise workflow. Characteristics: • Highly Data centric with huge compute demand. • Throughput and Bandwidth still important • AI Compute takes the center stage. • High Sensitivity towards network performance due to cloud architecture. Applications & Languages: • Traditional C,C++ and JAVA still has importance • Heavy use of new interpreter languages (Python, Ruby, Golang etc) • Applications are no longer monolithic. Moving towards microservices. • NoSQL databases takes dominance & unstructured data. Classical Enterprise Workloads Newer Generation Enterprise Workloads
- 10. What is thefoundation to make this transformation successful ?
- 11. 7 Pillars ofCompute Infrastructure • Semiconductor Technology Process • CPU – Architecture & Microarchitecture • Accelerators (GPUs, DSAs, FPGAs) • Memory • Interconnect • Storage • Network Performance at Scale Reliability & Availability Security
- 12. AI & Impacton Systems
- 13. AI Machine Learning/DeepLearning & Workload Motivation AI is impacting the entire computing stack Although AI computing is associated with traditional computation, it also has new computational characteristics, including: 1. The processing content is often unstructured data, such as video, image, voice and texts. 2. Processing usually requires a large amount of computation. The basic calculation is mainly linear algebraic operations, such as tensor processing, while the control flow is relatively simple. massively parallel compute hardware is more suitable. 3. Parameters are large, requiring huge storage capacity, high bandwidth, low latency memory access capacity, and rich and flexible connections between computing units and memory devices. Data localization is prevalent and suitable for data reuse and near memory computation.
- 14. AI acceleration –Hardware Design approaches 14 CPU / On-chip hardware acceleration for AI GPU/DSA/FPGA off-chip hardware acceleration for AI Flexibility Efficiency
- 15. POWER ISA &Matrix Math Assist (MMA) AI Acceleration on CPU (POWER Architecture)
- 16. ISA Examples • PowerPC •RISC V • SPARC • IA32 • IA64(Itanium) • ARM
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- 19. MMA Architecture • MMA architecture support is introduced in POWER ISA V3.1. • MMA architecture introduces new set of instructions to support dense matrix math operations along with required changes for register handling and management. •These Matrix-Multiply Assist instructions lead to very efficient implementations for key algorithms in technical computing, machine learning, deep learning and business analytics, it is a natural match for implementing dense numerical linear algebra computations • We have also shown application to other computations such as convolution • Scope for future research to support other computation in the area of arbitrary precision arithmetic, discrete Fourier transform etc.
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- 24. Outer Product using MMA • 1 accumulator(4×4 result) • 2 VSR loads/1 xv_ger instruction • 2 VSR load/ xv_ger
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