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The four realms of large-scale machine learning framework
Since Google published its groundbreaking papers on GFS, MapReduce, and BigTable, the internet officially entered the era of big data. The defining characteristics of big data are often summarized as "three Vs": volume, velocity, and variety. This article primarily focuses on the massive volume of data and explores architectural challenges in data processing through the lens of machine learning.
With the advent of GFS, it became possible to accumulate vast amounts of data—such as user exposure and click data from online advertisements. As a result, it's not uncommon to gather tens or even hundreds of billions of training samples within just a few months. Storing such massive datasets is no small feat, and choosing the right model to extract meaningful patterns from this sea of data presents both engineering and algorithmic challenges. These issues are not only technical but also require deep thinking from students and researchers alike.
**1.1 Simple Model vs. Complex Model**
Before the rise of deep learning, algorithm engineers had limited tools at their disposal. Models like logistic regression (LR), support vector machines (SVM), and perceptrons were widely used, but they were relatively simple and fixed in their structure. Feature engineering was the primary focus of many projects, yet there was no systematic theory guiding this process. As a result, feature construction often felt arbitrary, with success depending heavily on the specific problem, data, and even luck.
At that time, most feature engineering efforts failed to deliver improvements. Industry reports suggest that the success rate of new features in large companies rarely exceeded 20% in later stages. This meant that 80% of new features often had little to no positive impact. This approach could be described as "simple models + complex features"—where the model itself, such as LR or SVM, was linear and easy to interpret, while the features were constructed using various techniques like windowing, discretization, normalization, or Cartesian products.
The emergence of deep learning introduced a new paradigm: the ability of neural networks to perform representation learning. In image recognition, for example, CNNs could automatically extract high-level features, breaking previous performance ceilings. This shift led many to question the need for manual feature engineering.
However, deep learning does not eliminate all challenges. First, while it reduces the burden of feature engineering, it doesn't fully resolve it, especially in domains like personalized recommendation systems where deep learning has yet to achieve a clear advantage. Second, deep learning introduces new complexities, such as model interpretability and network design. Thus, the combination of "complex models + simple features" has become another viable approach.
Whether a simple model or a complex one is better depends on the context. For instance, in ad click prediction, large-scale LR with many features remains dominant due to its scalability. Meanwhile, in recommendation systems, deep learning frameworks like WDL and dual DNN have started to gain traction.
When models grow large, storing and training them becomes challenging. A single machine may struggle to hold parameters for an LR model with tens of billions of features, and neural networks introduce even more complexity. Distributed systems techniques are essential for handling these cases. This paper explores some of the key ideas behind large-scale machine learning frameworks.
**1.2 Data Parallel vs. Model Parallel**
Data parallelism and model parallelism are fundamental concepts in distributed machine learning. To understand them, consider a scenario where two buildings need repair. One approach is to split the team into two groups, each working on a building. The other is to have one group complete one building before moving to the second. The first method resembles data parallelism, where data is divided across machines, and each processes a subset. The second reflects model parallelism, where different parts of the model are processed in sequence.
Data parallelism is straightforward and works for any model. It involves distributing data across machines, updating model parameters locally, and aggregating results. This approach is widely used in frameworks like TensorFlow.
Model parallelism, on the other hand, is more complex. It requires managing dependencies between model components, which can vary significantly across architectures. Techniques like DAG scheduling are often used to coordinate these operations.
Understanding these concepts is crucial for grasping how parameter servers and distributed frameworks operate. The next section will explore the evolution of parallel algorithms.
**2. Evolution of Parallel Algorithms**
**2.1 MapReduce Approach**
Inspired by functional programming, Google introduced MapReduce as a distributed computing framework. By breaking tasks into Map and Reduce phases, it enabled efficient processing of large-scale data. However, MapReduce had limitations, including low-level primitives and disk-based data transfer, which affected performance.
To address these issues, Spark was developed, introducing RDDs and a higher-level abstraction. While Spark became popular for large-scale machine learning, it had bottlenecks, particularly in the Driver component. Frameworks like Angel extended Spark’s capabilities, pushing the boundaries further.
MapReduce, though outdated in some respects, remains influential as a conceptual framework for big data processing.
**2.2 MPI Technology**
MPI is a low-level communication API used for parallel computing. It supports message passing but lacks flexibility compared to higher-level frameworks. While it excels in system-level performance, it struggles with fault tolerance and scalability. Despite this, it still finds use in supercomputing environments.
**3. Parameter Server Evolution**
Parameter servers evolved from early distributed systems, starting with memory-based storage and progressing to general-purpose frameworks. Modern parameter servers must support efficient communication, flexible consistency models, scalability, fault tolerance, and ease of use.
The architecture typically includes a resource manager, a distributed file system, and core components like server groups and worker groups. Synchronization mechanisms are critical for ensuring consistent updates across workers.
As machine learning models continue to grow, the role of parameter servers becomes increasingly vital. Understanding their design and operation is essential for building scalable, efficient systems.