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80 lines
2.1 KiB
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80 lines
2.1 KiB
Markdown
---
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title: High Performance Computing 25 SP Potpourri
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date: 2025-08-31T13:51:29.8809980+08:00
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tags:
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- 高性能计算
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- 学习资料
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---
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Potpourri has a good taste.
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<!--more-->
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## Heterogeneous System Architecture
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The goals of the HSA:
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- Enable power efficient performance.
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- Improve programmability of heterogeneous processors.
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- Increase the portability of code across processors and platforms.
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- Increase the pervasiveness of heterogeneous solutions.
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### The Runtime Stack
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## Accelerated Processing Unit
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A processor that combines the CPU and the GPU elements into a single architecture.
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## Intel Xeon Phi
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The goal:
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- Leverage X86 architecture and existing X86 programming models.
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- Dedicate much of the silicon to floating point ops.
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- Cache coherent.
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- Increase floating-point throughput.
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- Strip expensive features.
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The reality:
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- 10s of x86-based cores.
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- Very high-bandwidth local GDDR5 memory.
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- The card runs a modified embedded Linux.
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## Deep Learning: Deep Neural Networks
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The network can used as a computer.
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## Tensor Processing Unit
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A custom ASIC for the phase of Neural Networks (AI accelerator).
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### TPUv1 Architecture
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### TPUv2 Architecture
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Advantages of TPU:
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- Allows to make predications very quickly and respond within fraction of a second.
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- Accelerate performance of linear computation, key of machine learning applications.
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- Minimize the time to accuracy when you train large and complex network models.
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Disadvantages of TPU:
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- Linear algebra that requires heavy branching or are not computed on the basis of element wise algebra.
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- Non-dominated matrix multiplication is not likely to perform well on TPUs.
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- Workloads that access memory using sparse technique.
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- Workloads that use highly precise arithmetic operations.
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