CcfindR And Rmpi: Fixing MacOS Installation Break

Hey guys! Let's dive into a tricky issue that's been causing headaches for ccfindR users on macOS. For several years, ccfindR has consistently failed to install on Bioconductor macOS builders, and it's all because of its dependency on Rmpi, which, unfortunately, isn't supported on macOS. This article will walk you through the problem, the reasons behind it, and the solutions to get ccfindR up and running smoothly on your Mac. We'll explore why Rmpi is causing issues, how you can work around it, and even look at a more robust solution using BiocParallel. So, buckle up and let's get started!

The Problem: ccfindR's Rocky Relationship with Rmpi on macOS

If you've been trying to install ccfindR on macOS via Bioconductor, you might have run into some frustrating errors. The core issue lies in ccfindR's dependency on the Rmpi package. Rmpi, which enables parallel computing in R using the Message Passing Interface (MPI), simply doesn't play well with macOS. This incompatibility has caused installation failures across multiple Bioconductor releases, from 3.17 all the way up to the current development version. This persistent problem has been highlighted in Bioconductor's check results, showing a clear pattern of installation failures specifically on macOS. The challenge arises because Rmpi's underlying infrastructure isn't fully supported on macOS, leading to build and runtime issues. For a package that aims to provide computational solutions, this dependency creates a significant bottleneck for Mac users. The history of this dependency can be traced back to a specific commit in 2018, where Rmpi was introduced as a core requirement for ccfindR. This decision, while intending to enhance performance through parallel processing, inadvertently excluded a segment of the user base reliant on macOS.

Why Rmpi on macOS is a No-Go

So, why is Rmpi causing so much trouble on macOS? The answer lies in the underlying architecture and system-level dependencies. Rmpi relies on MPI (Message Passing Interface), a standardized communication protocol for parallel computing. While MPI is widely used and supported on Linux and other Unix-like systems, its implementation on macOS can be problematic. macOS has its own set of libraries and system calls, and the integration of MPI isn't as seamless as on other platforms. This can lead to issues during the installation process, as the package struggles to link against the necessary system libraries. Furthermore, even if Rmpi manages to install, runtime errors can occur due to inconsistencies in the way macOS handles parallel processes compared to other operating systems. For developers and users, this means that relying on Rmpi can introduce a significant point of failure, especially when targeting a cross-platform audience. The complexity of setting up and maintaining Rmpi on macOS often outweighs the performance benefits it might offer, making it a less-than-ideal choice for packages intended for broad distribution.

The Root Cause: Tracing the Dependency Back

The introduction of Rmpi as a dependency for ccfindR dates back to a specific commit on June 18, 2018. This commit marked a pivotal change in the package's architecture, aiming to leverage parallel computing for enhanced performance. The intention behind this move was to speed up computationally intensive tasks within ccfindR by distributing the workload across multiple cores or processors. While the goal was laudable, the choice of Rmpi as the vehicle for parallelization introduced the macOS compatibility issue. Analyzing the commit reveals that the integration of Rmpi involved modifications to the core functions of ccfindR, specifically those dealing with iterative processes and data manipulation. The code was structured to utilize Rmpi's functions for spawning slave processes, broadcasting data, and collecting results. This direct dependency meant that any system lacking proper Rmpi support would face installation and runtime errors. The decision to incorporate Rmpi, although driven by performance considerations, highlights the importance of evaluating cross-platform compatibility when choosing dependencies. Understanding the historical context of this change helps to appreciate the challenges in resolving the issue while maintaining the performance gains that Rmpi was intended to provide.

Workaround 1: Making Rmpi an Optional Enhancement

One immediate solution to this predicament is to make Rmpi an optional dependency rather than a mandatory one. This approach involves moving Rmpi from the Imports section of the package's DESCRIPTION file to the Enhances section. By doing so, you're telling R that Rmpi is not essential for ccfindR's basic functionality but can enhance it when available. The key here is to modify the code to conditionally use Rmpi only when it's installed on the system. This can be achieved using the requireNamespace() function in R. This function checks if a package is installed and available before attempting to use it. By wrapping the Rmpi-dependent code in an if statement that uses requireNamespace(), you can ensure that it only runs if Rmpi is present. If Rmpi is not available, you can either fall back to a single-core implementation or provide a clear error message to the user. This approach allows ccfindR to function on systems without Rmpi, including macOS, while still leveraging parallel processing when possible. This strategy not only resolves the immediate installation issue but also provides a more flexible and user-friendly experience.

Here’s how you can modify the code:

Instead of:

if(ncores==1)
  vb <- lapply(seq_len(nrun), FUN=vb_iterate, bundle)
else{   # parallel
  Rmpi::mpi.spawn.Rslaves(nslaves=ncores)
  Rmpi::mpi.bcast.cmd(library(ccfindR))
  Rmpi::mpi.bcast.Robj2slave(bundle)
  vb <- Rmpi::mpi.applyLB(seq_len(nrun), FUN=vb_iterate, bundle)
  Rmpi::mpi.close.Rslaves()
  Rmpi::mpi.finalize()
}

Use:

if(ncores==1)
  vb <- lapply(seq_len(nrun), FUN=vb_iterate, bundle)
else if (requireNamespace("Rmpi", quietly=TRUE)) {   # parallel
  Rmpi::mpi.spawn.Rslaves(nslaves=ncores)
  Rmpi::mpi.bcast.cmd(library(ccfindR))
  Rmpi::mpi.bcast.Robj2slave(bundle)
  vb <- Rmpi::mpi.applyLB(seq_len(nrun), FUN=vb_iterate, bundle)
  Rmpi::mpi.close.Rslaves()
  Rmpi::mpi.finalize()
} else {
  stop("the Rmpi package is needed when 'ncores' is set to more than 1")
}

Workaround 2: Embracing BiocParallel for Universal Parallelism

While making Rmpi an optional dependency solves the immediate problem, a more robust and forward-looking solution is to replace Rmpi entirely with BiocParallel. BiocParallel is a Bioconductor package that provides a unified interface for parallel computing in R, supporting various backends such as multicore, MPI, and even cloud-based solutions. The beauty of BiocParallel is that it abstracts away the underlying parallelization mechanism, allowing your code to run on different platforms and environments without modification. By using BiocParallel::bplapply() instead of Rmpi's functions, you can achieve platform-independent parallel processing. This means your package will work seamlessly on macOS, Linux, and Windows, without the headaches associated with Rmpi. Moreover, BiocParallel integrates well with Bioconductor's infrastructure and provides additional features like logging and error handling, making it a superior choice for Bioconductor packages. Transitioning to BiocParallel not only resolves the macOS issue but also future-proofs your package against potential compatibility problems with other parallelization libraries. This approach aligns with best practices in scientific computing, promoting reproducibility and portability of research.

Why BiocParallel is the Superior Solution

Choosing BiocParallel over Rmpi offers several compelling advantages, especially within the Bioconductor ecosystem. First and foremost, BiocParallel is designed to work seamlessly across different operating systems, including macOS, Linux, and Windows. This cross-platform compatibility eliminates the installation and runtime issues that plague Rmpi users on macOS. Secondly, BiocParallel provides a consistent and unified interface for parallel computing, regardless of the underlying backend. Whether you're using multicore processing on a single machine or distributing tasks across a cluster with MPI, your code remains the same. This abstraction simplifies development and maintenance, as you don't need to write platform-specific code. Furthermore, BiocParallel integrates tightly with Bioconductor's infrastructure, offering features like automatic resource management and sophisticated error handling. It also supports various parallelization backends, including multicore, SnowParam, and BatchJobs, giving you flexibility in choosing the most appropriate solution for your computing environment. By adopting BiocParallel, you're not just fixing a compatibility issue; you're embracing a more robust, flexible, and future-proof approach to parallel computing in R. This decision aligns with Bioconductor's mission of promoting reproducible and scalable research.

Conclusion: A Path Forward for ccfindR

In conclusion, the dependency on Rmpi has been a persistent stumbling block for ccfindR on macOS, causing installation failures and limiting its accessibility. However, as we've explored, there are clear paths forward. Making Rmpi an optional enhancement provides an immediate workaround, allowing ccfindR to function on macOS while still leveraging parallel processing when available. But the most robust and future-proof solution lies in adopting BiocParallel. By transitioning to BiocParallel, ccfindR can achieve true cross-platform compatibility, simplify its codebase, and integrate seamlessly with the Bioconductor ecosystem. This shift not only resolves the current macOS issue but also positions ccfindR for broader adoption and long-term maintainability. So, let's embrace BiocParallel and ensure that ccfindR can thrive on all platforms, empowering researchers with its powerful capabilities. Remember, choosing the right dependencies and tools is crucial for creating software that is both performant and accessible to a wide audience.

For more information on BiocParallel, check out the official Bioconductor documentation: BiocParallel.