Parallel Programming Archetypes in Combinatorics and Optimization
Read PDF →Kryukova, 1995
Category: Parallel Computing
Overall Rating
Score Breakdown
- Cross Disciplinary Applicability: 4/10
- Latent Novelty Potential: 3/10
- Obscurity Advantage: 3/5
- Technical Timeliness: 2/10
Synthesized Summary
This paper presents a commendable early attempt to formalize parallel programming patterns (archetypes) and integrate performance modeling into their design.
However, the specific parallel implementation strategies (Data Flow, Master-Slave Branch and Bound) and the performance analysis framework are deeply tied to the hardware and software landscapes of the mid-1990s...
Modern parallel programming libraries, task-based frameworks, and highly optimized problem-specific solvers offer significantly more scalable, portable, and productive approaches, rendering the specific methodologies detailed in this paper largely obsolete...
While the conceptual goal of structured, performance-aware parallel design remains relevant, this paper does not provide a unique, actionable path forward for modern researchers compared to current state-of-the-art methods.
Optimist's View
This paper proposes a structured approach to parallel programming using "archetypes," which are language-independent design strategies embodying common algorithmic patterns like Divide-and-Conquer (DnC) and Branch and Bound (BnB).
Beyond merely presenting algorithmic skeletons, the paper defines the components of these archetypes formally using data types, procedures, and logical predicates...
A specific, unconventional research direction inspired by this paper could focus on leveraging its structured, formally-informed archetype methodology for designing and automatically optimizing parallel algorithms for computationally intensive, unstructured search problems...
By treating components like Branch, Bound, and inter-process communication as parameterized operations, modern empirical auto-tuning techniques and machine learning could be applied to automatically determine optimal parallel strategies...
Skeptic's View
The fundamental flaw lies in the strong coupling of the proposed Data Flow approach (the focus of the implementation details and performance analysis) to the computational and communication characteristics of early-to-mid 1990s parallel hardware.
The requirement for users to define custom Problem_t, Solution_t, and OtherInfo_t types, along with manual Split, Merge, and crucially, data transfer functions... for each new problem type is a major barrier.
Modern parallel programming frameworks and libraries have absorbed the useful aspects of these patterns while providing superior abstractions and performance:
Attempting to directly apply these 1995 archetypes to cutting-edge fields like AI... Quantum Computing, or modern Biotech simulations would be a significant misdirection of effort:
Final Takeaway / Relevance
Ignore