Is Parallel Programming Really That Difficult?

Everyone uses parallel programming but not everyone loves it.

Ask any developer and they will tell you how parallel programming has helped them boost their productivity, perform complex tasks and more. But, there are a few of them who will tell you that parallel programming is difficult to learn, master and implement correctly. 

Ironically, parallel programming is one of the best ways to solve complex problems. It involves breaking a complex task down into smaller subtasks that may each be carried out concurrently by multiple processing units, such as processors or cores. 

Large language models like Transformer, BERT, and GPT-3.5 use parallel computing to speed up their training and inference processes, such as on multiple TPUs or GPUs. Training such models involves computationally intensive tasks with huge data sets while updating numerous model parameters. Besides that, such models also use parallel processing to generate responses or make predictions quickly. For example, ChatGPT uses parallel programming to process data faster and answer user queries in real time.

Parallel processing based on asynchronous execution eventually led to the introduction of the data centre for scale computing. Programmers should consider both data parallelism and data locality to channelise the full potential of data centres and other parallel computing systems. 

Parallel computing is like a ladder of different steps, with each step aiding to make the programme run better. It is the precursor to supercomputing as it uses multiple processing units concurrently to solve complex problems. CUDA developer NVIDIA unveiled its open, unified computing platform, ‘QODA’ (quantum optimised device architecture) in 2022 with the aim to foster quantum research and development across various areas, including AI, HPC, health, finance and others. 

Read more: NVIDIA wants to replicate CUDA success with Quantum Computing

Then, what’s the problem? 

According to the developer community on Reddit, the ultimate challenge of parallel programming is latency, besides getting synchronous state to fit into L1/2 which can be helped by processing transactions in contiguous data batches.

Latency is the delay that occurs when transferring data between components. For instance, the delay between a processor and memory or a client and a server. Latency can affect the performance of parallel programming, as it can cause delays in the communication between processors or cores. Parallel programming employs techniques such as data partitioning, load balancing, and message passing, which aim to foster communication between processing elements, and reduce the time required waiting for data to be transferred. High performance programming can be difficult, especially when employing memory barriers or lock-free programming styles. 

Read more: Quantum Computing Meets ChatGPT

Another challenge with parallel programming is that not all types of programmes are well-suited to parallelisation. Some programmes have dependencies or interactions between different parts that make it difficult or impossible to run them in parallel, which can limit the benefits of parallel programming.

There are several parallel processing platforms that aim to make it easy like CUDA (by NVIDIA), OpenCL (by Khronos Group), OpenMP, and Intel TBB. When you use such a platform, it becomes easier to work on multiple systems simultaneously and write parallel code using familiar programming languages, compared to programming without a platform. These platforms are helpful in managing the complexity of parallel code, by providing features such as load balancing, data distribution, and synchronisation.

However, parallel programming can still be challenging due to issues involving race conditions, deadlocks, and load imbalance. In addition, debugging and performance-tuning parallel programmes can be more difficult than sequential programmes.

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Shritama Saha
Shritama is a technology journalist who is keen to learn about AI and analytics play. A graduate in mass communication, she is passionate to explore the influence of data science on fashion, drug development, films, and art.

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