Table of Contents
The Concurrency with Python Series:
- Concurrency with Python: Why?
- Concurrency with Python: Threads and Locks
- Concurrency with Python: Functional Programming
- Concurrency with Python: Separating Identity From State
- Concurrency with Python: Actor Models
- Concurrency with Python: CSP and Coroutines
- Concurrency with Python: Hardware-Based Parallelism
- Concurrency with Python: Data-Intensive Architectures
- Concurrency with Python: Conclusion
If there is one concurrency model that makes Python one of the dominant programming languages of today, it’s hardware-based parallelism. Python’s C/C++ API, backed by an extensive integration tutorial, transforms Python from a general-purpose scripting language into a data orchestration language. This, combined with the superlinearly increasing value prop differentiation between companies that sanctify data and those that do not, make Python and its ecosystem very much worth investing in.
Python excels with hardware-based parallelism for a number of reasons:
A complementary development model: Python as a language is rather simple. Many design decisions around the language (such as the lack of tail recursion elimination, mentioned in the discussion on functional programming) keep writing and debugging Python code as simple, quick, and painless as possible.
In contrast, many low-level, high-performance libraries are quite complicated. For example, Michael Lehn wrote a great Stack Overflow answer on how BLAS implementations take advantage of CPU microarchitectural optimizations by segmenting functions based on their inputs (vector-vector, matrix-vector, matrix-matrix), and rewriting functions from scratch instead of composing them from lower-level functions by using specialized CPU instructions when possible.
Python composes these low-level libraries to eliminate this tradeoff between application performance and development velocity. Stable code susceptible to performance bottlenecks can be rewritten in a lower-level language and represented as a Python library. Python glue code ties these libraries together, keeping most errors in Python and easily patchable. Low-level libraries can also have their own library bindings between each other, which reduces the need to propagate control flow to Python for performance-sensitive work while still separating concerns.
The GIL as a forcing function: Removing the GIL from CPython is not possible without decreasing performance or correctness. This
pypyblog post aptly explains why. It’s easier to simply work around the GIL rather than to try and remove it entirely, and amortize the costs of spending time in Python-land instead of reducing the cost of time spent in Python-land. Hence, the GIL acts as a forcing function for the Python community to find other ways to increase performance, which may unify/increase investment into the C/C++ API and the interfacing ecosystem around hardware-based parallelism.
There are a number of serious downsides to applying hardware-based parallelism as a concurrency model, which should be carefully considered before deploying hardware-accelerated Python to production:
Speedups may be marginal and difficult to measure: For embarassingly parallel problems (like deep learning), or problems with dedicated hardware instructions, hardware-based parallelism makes sense since there are fewer resource bounds. For example, on some graphics-oriented GPU microbenchmarks, such as
CUDA_brute_triangle, you may see speedups well within two orders of magnitude. However, this may not extrapolate to your specific workload. For example, Robert Crovella on NVIDIA DevTalk discusses how speedups of more than 5x may be suspicious for CPU-bound or memory-bound work, likely because production workloads have different resource bounds over time that reduce hardware resource utilization.
Comparing speedups between different hardware targets is also difficult; parameter tuning and/or source rewrites generate process differences for different hardware targets. Benchmarks may also be made political if sales/revenue of hardware vendors are at stake. For example, The Inquirer published a dissension between NVIDIA and Intel, detailing the need to consider price and energy usage in addition to performance for a holistic judgment about the “right” solution for machine learning.
Lack of portability: Relying on bare-metal primitives ties any dependent Python code to the hardware target. Hence, porting across heterogeneous deployment environments is no longer a trivial matter as the code may depend on specific operating systems or hardware architectures.
Sometimes the code is portable, but workload behavior unexpectedly changes depending on the available hardware. One deployment of
hypothesisI made for an open source project caused an unexpected slowdown during CI/CD testing, likely because the virtual machine the CI/CD worker was deployed on top of lacked access to hardware-based cryptographic acceleration, causing significant slowdowns during integration testing and a misalignment in technical expectations within the development team.
Finally, the lack of portability precludes easy horizontal scaling, absent higher-level tooling and infrastructure. This reduces effective commoditization of complements and possibly contributes to hardware price increases. Adrian Rosebrock quoted the NVIDIA DIGITS DevBox, a highly capable piece of equipment used for professional deep learning, at around $15,000. This is a steal in comparison to doing the same work with cloud-based GPUs.
The state of Linux device drivers: Artem Tashkinov wrote a great blog post on the difficulties of working with Linux. One primary difficulty raised is Linux GPU device driver compatibility, and how NVIDIA oftentimes patches around its own API specification. Since CUDA is the only commercially viable GPGPU library, this may have outstanding effects on GPGPU use cases.
Difficult API development: Performance relies on ensuring as much processing occurs within the lower-level code without interruption. Hence, performance may be made more efficient and predictable to build a massive request and flush it at once through
ffithan it is to piece together multiple Python API requests and having to continually move between Python and its low-level bindings. This may make it more difficult to design explainable, stable, and simple APIs. The method
pandas.read_csv()may act as a weak signal in this regard; its function signature has 48 optional parameters.
async/awaitfeature in particular, one limiting factor around
async/awaitadoption by Python libraries leveraging the C/C++ API is the lack of portability of Python’s
get_event_loop()feature to the C API. This Python mailing list response by Yury Selivanov should still be relevant today. Without endorsed
async/awaitbridging code conventions, Python libraries around I/O-bound tasks needed to re-implement major functionality from older libraries, resulting in partial ecosystem fragmentation.
Forms of Hardware
Single instruction, multiple data (SIMD)
is a hardware paradigm where atomic CPU instructions process on multiple chunks
of data in parallel. Some compilations of
numpy can apply SIMD to
structures via vectorization
This may be possible via Intel’s
AVX extensions, and
Intel’s special compilation of
SIMD can be further composed in hardware architectures to become multiple instructions, multiple data, where multiple processors execute SIMD instructions.
SIMD enforces lockstep where each chunk of data must be processed at the same time as other data chunks. Strictly speaking, this means SIMD is parallel but not concurrent; asynchronicity is not possible with SIMD.
Single instruction, multiple threads (SIMT) is an execution model where single instructions execute on data passing through threads in multiple discrete processors, as opposed to SIMD lanes within a single processor. GPGPUs leverage SIMT to process large quantities of data with many simple processors.
One interesting aspect of GPU architectures is that the instruction set
significantly between generations of GPUs, as noted in this NVIDIA DevTalk
This contrasts heavily with CPUs, which strongly prioritize binary-code
compatibility and ISA
stability. Hence, software targeting GPUs leverage a high-level library such as
CUDA that can abstract away targeting multiple GPU generations. This may imply
software compiled for GPU targets may particularly benefit from concolic
angr is one Python-based concolic testing
GKLEE is a
concolic testing tool targeted towards GPUs.
Application-specific integrated circuits optimize for a specific workload, granting benefits like performance speedups or cost reductions compared to other hardware acceleration targets at the cost of vendor lock-in. Major ASIC developments, such as TPUs, have evolved from GPU-based workloads, where additional performance met benefit/cost thresholds.
Python’s ability to leverage hardware-based parallelism, and the resultant development of highly performant and composable libraries, is a big reason behind its relevance today and why user adoption is “sticky”. However, it makes deploying and reproducing the benefits gained from hardware-based parallelism difficult. This problem already presents itself as a realized business opportunity. For example, h2o.ai eases the process of Jupyter notebook deployment, which removes the last-mile blockers between data science and business impact. As long as Python’s lower-level bindings exist, Python can be made performant.