Ask any developer and they’ll tell you how parallel programming has helped them increase 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. A complex task is broken down into smaller subtasks, each of which can be executed simultaneously 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, for example on multiple TPUs or GPUs. Training such models involves computationally intensive tasks with huge data sets while updating numerous model parameters. In addition, such models also use parallel processing to quickly generate answers or make predictions. For example, ChatGPT uses parallel programming to process data faster and respond to user queries in real time.
Parallel processing based on asynchronous execution eventually led to the advent of the data center for scale computing. Programmers should consider both data parallelism and data locality to realize the full potential of data centers and other parallel computing systems.
Parallel computing is like a ladder with different steps, each step helping the program run better. It is the forerunner of supercomputing as it uses multiple processing units simultaneously to solve complex problems. CUDA developer NVIDIA unveiled its open unified computing platform “QODA” (Quantum Optimized Device Architecture) in 2022 with the goal of boosting quantum research and development in various fields including AI, HPC, health, finance and others.
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Read more: NVIDIA looks to repeat CUDA success with Quantum Computing
What is the problem then?
According to the developer community on Reddit, the ultimate challenge of parallel programming is latency, apart from fitting synchronous state in L1/2, which can be helped by processing transactions in contiguous batches of data.
Latency is the delay that occurs when data is transferred between components. For example, the delay between a processor and a memory or a client and a server. Latency can affect the performance of parallel programming because it can cause delays in communication between processors or cores. Parallel programming uses techniques such as data partitioning, load balancing, and message passing that aim to encourage communication between processing elements and reduce latency for data to be transmitted. High performance programming can be difficult, especially when using memory barriers or lock-free programming styles.
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Another challenge with parallel programming is that not all types of programs lend themselves well to parallelization. Some programs have dependencies or interactions between different parts that make them difficult or impossible to run in parallel, which can limit the benefits of parallel programming.
There are several parallel processing platforms that aim to make it easy, such as CUDA (by NVIDIA), OpenCL (by Khronos Group), OpenMP, and Intel TBB. Using such a platform makes it easier to work on multiple systems at the same time and write parallel code using familiar programming languages compared to programming without a platform. These platforms help manage the complexity of parallel code by providing features such as load balancing, data distribution, and synchronization.
However, parallel programming can still be challenging due to issues related to race conditions, deadlocks, and load imbalances. Also, parallel programs can be more difficult to debug and tune for performance than sequential programs.
