The blog post explores various methods for generating Static Single Assignment (SSA) form, a crucial intermediate representation in compilers. It starts with the basic concepts of SSA, explaining dominance and phi functions. Then, it delves into different algorithms for SSA construction, including the classic dominance frontier algorithm and the more modern Cytron et al. algorithm. The post emphasizes the performance implications of these algorithms, highlighting how Cytron's approach optimizes placement of phi functions. It also touches upon less common methods like the iterative and memory-efficient Chaitin-Briggs algorithm. Finally, it briefly discusses register allocation and how SSA simplifies this process by providing a clear data flow representation.
The blog post argues for an intermediate representation (IR) layer in query compilers between the logical plan and the physical plan, called the "relational algebra IR." This layer would represent queries in a standardized, relational algebra form, enabling greater portability and reusability of optimization rules across different physical execution engines. Currently, optimization logic is often tightly coupled to specific physical plans, making it difficult to adapt to new engines or hardware. By introducing this standardized relational algebra IR, query compilers can achieve better modularity and extensibility, simplifying development and allowing for easier experimentation with new optimization strategies without needing to rewrite code for each backend. This ultimately leads to more efficient query execution across diverse environments.
HN commenters generally agree with the author's premise that a middle tier is missing in query compilers, sitting between logical optimization and physical optimization. This tier would handle "cross-physical plan" optimizations, allowing for better cost-based decisions that consider different physical plan choices holistically rather than sequentially. Some discuss the challenges in implementing this, particularly the explosion of search space and the difficulty in accurately costing plans. Others offer specific examples where such a tier would be beneficial, such as selecting join algorithms based on data distribution or optimizing for specific hardware like GPUs. A few commenters mention existing systems that implement similar concepts, though not necessarily as a distinct tier, suggesting the idea is already being explored in practice. Some debate the practicality of the proposed solution, suggesting alternative approaches like adaptive query execution or learned optimizers.
Yasser is developing "Tilde," a new compiler infrastructure designed as a simpler, more modular alternative to LLVM. Frustrated with LLVM's complexity and monolithic nature, he's building Tilde with a focus on ease of use, extensibility, and better diagnostics. The project is in its early stages, currently capable of compiling a subset of C and targeting x86-64 Linux. Key differentiating features include a novel intermediate representation (IR) designed for efficient analysis and transformation, a pipeline architecture that facilitates experimentation and customization, and a commitment to clear documentation and a welcoming community. While performance isn't the primary focus initially, the long-term goal is to be competitive with LLVM.
Hacker News users discuss the author's approach to building a compiler, "Tilde," positioned as an LLVM alternative. Several commenters express skepticism about the project's practicality and scope, questioning the rationale behind reinventing LLVM, especially given its maturity and extensive community. Some doubt the performance claims and suggest benchmarks are needed. Others appreciate the author's ambition and the technical details shared, seeing value in exploring alternative compiler designs even if Tilde doesn't replace LLVM. A few users offer constructive feedback on specific aspects of the compiler's architecture and potential improvements. The overall sentiment leans towards cautious interest with a dose of pragmatism regarding the challenges of competing with an established project like LLVM.
Summary of Comments ( 31 )
https://news.ycombinator.com/item?id=43009952
HN users generally agreed with the author's premise that Single Static Assignment (SSA) form is beneficial for compiler optimization. Several commenters delved into the nuances of different SSA construction algorithms, highlighting Cytron et al.'s algorithm for its efficiency and prevalence. The discussion also touched on related concepts like minimal SSA, pruned SSA, and the challenges of handling irreducible control flow graphs. Some users pointed out practical considerations like register allocation and the trade-offs between SSA forms. One commenter questioned the necessity of SSA for modern optimization techniques, sparking a brief debate about its relevance. Others offered additional resources, including links to relevant papers and implementations.
The Hacker News post titled "Ways to generate SSA" (https://news.ycombinator.com/item?id=43009952) discusses various methods for generating Static Single Assignment (SSA) form, as described in the linked blog post. The comments section contains several insightful contributions, focusing primarily on the practicalities and nuances of SSA implementation.
One commenter points out that the blog post uses an unconventional definition of dominance, focusing on dominance frontiers rather than the typical understanding of dominance relations in compiler design. This commenter suggests that the approach described in the blog post isn't technically generating SSA in the traditional sense, but rather a variant that directly calculates liveness information. This sparked a brief discussion about the different perspectives on dominance and how they relate to SSA construction.
Another significant thread discusses the performance implications of different SSA construction algorithms. One commenter highlights the Cytron et al. algorithm as a particularly efficient approach. This led to a further discussion about the trade-offs between different algorithms, with some commenters arguing that simpler algorithms can be more practical in certain scenarios, despite potentially being less theoretically optimal. Specific mention is made of the impact on register allocation and the complexities introduced by handling exceptions and other control flow irregularities.
Furthermore, the discussion touches upon the challenges of implementing SSA in real-world compilers. One commenter shares their personal experience working on the V8 JavaScript engine, noting that the performance benefits of SSA can be substantial, but that the actual implementation can be quite complex due to the need to handle JavaScript's dynamic nature and features like
eval. Another commenter mentions the prevalence of SSA in modern optimizing compilers, reinforcing its importance in achieving high performance.Finally, some comments provide additional context and resources related to SSA. One commenter links to a relevant Wikipedia article, while another recommends a specific chapter in the "Engineering a Compiler" textbook for further reading. These comments serve to broaden the discussion and provide valuable learning resources for those interested in delving deeper into the topic of SSA.