Date: Sun, 10 Nov 2024 18:28:47 -0500
On 11/8/24 03:35, Jens Maurer wrote:
>
>
> On 08/11/2024 01.23, Phil Bouchard via Std-Discussion wrote:
>> This is not a proposal but an important factor people dismiss which the power consumption of programming languages in this AI era. Basically there would be no power consumption issues if C++ was used instead of Python for AI algorithms.
>
> The core of AI algorithms is not written in Python.
> What is written in Python is the driver logic around it.
>
> See, for example, https://github.com/numpy/numpy/tree/main/numpy/_core/src/npymath
> for (a portion of) the C part of numpy, a popular package for
> numeric computing with Python.
>
> Can you point to a data-backed analysis that shows the current practice
> of using Python for the AI driver logic leaves room for substantial
> performance gains?
There are still plenty of room for further optimizations, ironically
memory management being top #1:
Yes, even with extensive optimization, there are still many areas where
C/C++ performance in frameworks like PyTorch and TensorFlow can be
further improved. Here are some key areas where ongoing optimizations
are common and can bring significant gains:
### 1. **Memory Management and Data Movement**
- **Optimized Memory Allocation**: Minimizing memory allocation and
deallocation overhead, especially for dynamic-sized data structures, can
yield gains. Techniques like **memory pooling** and **arena allocators**
are used, but further customization can reduce latency in memory-bound
operations.
- **Efficient Data Transfer**: Moving data between CPU and GPU is
costly. By improving memory transfer methods (e.g., using **zero-copy
memory** or **unified memory** on compatible systems), data can be
shared between devices with minimal overhead.
### 2. **Parallelism and Concurrency**
- **Better Threading Models**: Libraries often rely on
multithreading libraries (like OpenMP, TBB) for parallel execution.
However, optimizing thread scheduling and workload balancing at the C++
level can improve performance, particularly in multi-core or many-core
environments.
- **Asynchronous Execution**: Effective use of asynchronous
processing (e.g., streams in CUDA) can overlap data transfers with
computation, reducing idle time.
### 3. **Specialized Hardware Instructions**
- **Vectorization (SIMD)**: Further optimization using **SIMD**
instructions (e.g., AVX-512 on Intel, SVE on ARM) can accelerate linear
algebra operations. Many frameworks already use SIMD, but specialized
implementations can sometimes improve specific operations like
convolution or matrix multiplication.
- **FP16 and BFLOAT16 Support**: Reducing precision (where
appropriate) to half-precision (FP16) or **BFLOAT16** can accelerate
training while using less memory, especially on hardware that supports
it, like NVIDIA Tensor Cores or Google TPUs.
### 4. **Optimized Graph Execution**
- **Kernel Fusion**: By merging multiple operations into a single
kernel (called kernel fusion), frameworks can reduce memory access
overhead and improve cache efficiency.
- **Dynamic Shape Optimization**: When dealing with models where
tensor shapes change frequently, optimizing the handling of dynamic
shapes (e.g., shape inference and caching mechanisms) can improve
execution speed.
### 5. **Distributed and Multi-GPU Communication**
- **Faster Inter-GPU Communication**: Optimizing collective
operations (like all-reduce) for multi-GPU setups, possibly with
**NCCL** for GPU clusters, can improve training efficiency in
distributed systems.
- **Hybrid Parallelism**: Balancing data parallelism with model
parallelism, pipeline parallelism, and other parallelism strategies can
reduce bottlenecks in distributed training on large models.
### 6. **Custom Compiler Optimizations and JIT Enhancements**
- **Further Optimizing JIT Compilation**: Libraries like
TensorFlow’s **XLA** or PyTorch’s **TorchScript** can be further tuned
to optimize kernels for specific hardware configurations, even beyond
standard compilation.
- **Ahead-of-Time (AOT) Compilation**: Where applicable, AOT
compilation can be used instead of JIT to avoid compilation overhead
during execution and to optimize kernel placement for specific hardware.
### 7. **Numerical Stability and Precision Improvements**
- **Precision Handling**: Improving floating-point arithmetic
handling can mitigate precision issues. Libraries can be optimized to
dynamically choose the best precision for each operation, allowing for
efficient low-precision operations without compromising accuracy.
- **Stable Algorithms**: Implementing numerically stable algorithms,
especially in operations like matrix inversion, can improve both speed
and accuracy, as fewer correction steps are needed.
### 8. **Custom Libraries and Pruning**
- **Sparse Matrix Operations and Model Pruning**: By optimizing
sparse matrix operations and leveraging **model pruning** (removing
unnecessary weights), libraries can improve speed and reduce memory
consumption, especially for large neural networks.
- **Custom C++ Kernels**: In cases where general-purpose libraries
are suboptimal, specialized C++ kernels tailored to specific operations
or hardware setups can yield further improvements.
### 9. **Exploring Novel Hardware**
- **Adapting for New Hardware Architectures**: The landscape of
accelerators (like IPUs, Graphcore, Cerebras, or quantum computing) is
expanding. Custom optimizations in C++ libraries to adapt to these
architectures can offer significant speedups.
In summary, while deep learning libraries are already heavily optimized,
there are still gains to be made by further improving memory management,
parallel execution, hardware utilization, and custom kernel
optimizations. Each new generation of hardware and advances in
algorithmic research open new doors for C/C++ optimizations in
high-performance computing.
>
>
> On 08/11/2024 01.23, Phil Bouchard via Std-Discussion wrote:
>> This is not a proposal but an important factor people dismiss which the power consumption of programming languages in this AI era. Basically there would be no power consumption issues if C++ was used instead of Python for AI algorithms.
>
> The core of AI algorithms is not written in Python.
> What is written in Python is the driver logic around it.
>
> See, for example, https://github.com/numpy/numpy/tree/main/numpy/_core/src/npymath
> for (a portion of) the C part of numpy, a popular package for
> numeric computing with Python.
>
> Can you point to a data-backed analysis that shows the current practice
> of using Python for the AI driver logic leaves room for substantial
> performance gains?
There are still plenty of room for further optimizations, ironically
memory management being top #1:
Yes, even with extensive optimization, there are still many areas where
C/C++ performance in frameworks like PyTorch and TensorFlow can be
further improved. Here are some key areas where ongoing optimizations
are common and can bring significant gains:
### 1. **Memory Management and Data Movement**
- **Optimized Memory Allocation**: Minimizing memory allocation and
deallocation overhead, especially for dynamic-sized data structures, can
yield gains. Techniques like **memory pooling** and **arena allocators**
are used, but further customization can reduce latency in memory-bound
operations.
- **Efficient Data Transfer**: Moving data between CPU and GPU is
costly. By improving memory transfer methods (e.g., using **zero-copy
memory** or **unified memory** on compatible systems), data can be
shared between devices with minimal overhead.
### 2. **Parallelism and Concurrency**
- **Better Threading Models**: Libraries often rely on
multithreading libraries (like OpenMP, TBB) for parallel execution.
However, optimizing thread scheduling and workload balancing at the C++
level can improve performance, particularly in multi-core or many-core
environments.
- **Asynchronous Execution**: Effective use of asynchronous
processing (e.g., streams in CUDA) can overlap data transfers with
computation, reducing idle time.
### 3. **Specialized Hardware Instructions**
- **Vectorization (SIMD)**: Further optimization using **SIMD**
instructions (e.g., AVX-512 on Intel, SVE on ARM) can accelerate linear
algebra operations. Many frameworks already use SIMD, but specialized
implementations can sometimes improve specific operations like
convolution or matrix multiplication.
- **FP16 and BFLOAT16 Support**: Reducing precision (where
appropriate) to half-precision (FP16) or **BFLOAT16** can accelerate
training while using less memory, especially on hardware that supports
it, like NVIDIA Tensor Cores or Google TPUs.
### 4. **Optimized Graph Execution**
- **Kernel Fusion**: By merging multiple operations into a single
kernel (called kernel fusion), frameworks can reduce memory access
overhead and improve cache efficiency.
- **Dynamic Shape Optimization**: When dealing with models where
tensor shapes change frequently, optimizing the handling of dynamic
shapes (e.g., shape inference and caching mechanisms) can improve
execution speed.
### 5. **Distributed and Multi-GPU Communication**
- **Faster Inter-GPU Communication**: Optimizing collective
operations (like all-reduce) for multi-GPU setups, possibly with
**NCCL** for GPU clusters, can improve training efficiency in
distributed systems.
- **Hybrid Parallelism**: Balancing data parallelism with model
parallelism, pipeline parallelism, and other parallelism strategies can
reduce bottlenecks in distributed training on large models.
### 6. **Custom Compiler Optimizations and JIT Enhancements**
- **Further Optimizing JIT Compilation**: Libraries like
TensorFlow’s **XLA** or PyTorch’s **TorchScript** can be further tuned
to optimize kernels for specific hardware configurations, even beyond
standard compilation.
- **Ahead-of-Time (AOT) Compilation**: Where applicable, AOT
compilation can be used instead of JIT to avoid compilation overhead
during execution and to optimize kernel placement for specific hardware.
### 7. **Numerical Stability and Precision Improvements**
- **Precision Handling**: Improving floating-point arithmetic
handling can mitigate precision issues. Libraries can be optimized to
dynamically choose the best precision for each operation, allowing for
efficient low-precision operations without compromising accuracy.
- **Stable Algorithms**: Implementing numerically stable algorithms,
especially in operations like matrix inversion, can improve both speed
and accuracy, as fewer correction steps are needed.
### 8. **Custom Libraries and Pruning**
- **Sparse Matrix Operations and Model Pruning**: By optimizing
sparse matrix operations and leveraging **model pruning** (removing
unnecessary weights), libraries can improve speed and reduce memory
consumption, especially for large neural networks.
- **Custom C++ Kernels**: In cases where general-purpose libraries
are suboptimal, specialized C++ kernels tailored to specific operations
or hardware setups can yield further improvements.
### 9. **Exploring Novel Hardware**
- **Adapting for New Hardware Architectures**: The landscape of
accelerators (like IPUs, Graphcore, Cerebras, or quantum computing) is
expanding. Custom optimizations in C++ libraries to adapt to these
architectures can offer significant speedups.
In summary, while deep learning libraries are already heavily optimized,
there are still gains to be made by further improving memory management,
parallel execution, hardware utilization, and custom kernel
optimizations. Each new generation of hardware and advances in
algorithmic research open new doors for C/C++ optimizations in
high-performance computing.
-- Fornux Logo <https://www.fornux.com/> *Phil Bouchard* LinkedIn Icon <https://www.linkedin.com/in/phil-bouchard-5723a910/> Founder & CEO T: (819) 328-4743 E: phil_at_[hidden]| www.fornux.com <http://www.fornux.com> 320-345 de la Gauchetière Ouest| Montréal (Qc), H2Z 0A2 Canada The Best Predictable C++ Memory Manager <https://static.fornux.com/c-superset/> Le message ci-dessus, ainsi que les documents l'accompagnant, sont destinés uniquement aux personnes identifiées et peuvent contenir des informations privilégiées, confidentielles ou ne pouvant être divulguées. Si vous avez reçu ce message par erreur, veuillez le détruire. This communication (and/or the attachments) is intended for named recipients only and may contain privileged or confidential information which is not to be disclosed. If you received this communication by mistake please destroy all copies.
Received on 2024-11-10 23:28:49