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Bhavisha Vishalbhai Parvadiya,
- Assistant Professor, , Department of Computer Science and Engineering, Sardar Patel College of Administration and Management, Gujarat, India
Abstract
The accelerating demand for computational power in scientific, engineering, and data-intensive domains has driven High-Performance Computing (HPC) systems toward unprecedented levels of parallelism and architectural complexity. Contemporary HPC platforms integrate multicore CPUs, many-core GPUs, accelerators, and deep memory hierarchies, creating significant challenges for software development and performance optimization. Traditional general-purpose programming languages and parallel programming frameworks provide low-level control over hardware resources but require extensive manual tuning, resulting in poor productivity and limited performance portability. Domain-Specific Languages (DSLs) have emerged as a transformative approach to HPC software development by offering high-level abstractions that encode domain knowledge directly into language constructs and compiler infrastructures. This enables aggressive, semantics-aware optimizations that are difficult or impossible to achieve using conventional programming paradigms. This paper presents a comprehensive and deeply analytical study of the design principles, classification, and optimization techniques of DSLs for HPC applications. It further examines prominent DSL frameworks through detailed case studies, analyzes their performance benefits, and discusses open challenges and future research directions in the context of exascale and energy-aware computing.
Keywords: Domain-Specific Languages, High-Performance Computing, Compiler Optimization, Parallel Architectures, Performance Portability, Heterogeneous Systems
Bhavisha Vishalbhai Parvadiya. Design and Optimization of Domain-Specific Languages for High-Performance Computing Applications. Recent Trends in Parallel Computing. 2026; 13(01):-.
Bhavisha Vishalbhai Parvadiya. Design and Optimization of Domain-Specific Languages for High-Performance Computing Applications. Recent Trends in Parallel Computing. 2026; 13(01):-. Available from: https://journals.stmjournals.com/rtpc/article=2026/view=242290
References
- Kirk DB, Wen-Mei WH. Programming massively parallel processors: a hands-on approach. Morgan kaufmann; 2016 Nov 24.
- Dongarra J, Beckman P, Moore T, Aerts P, Aloisio G, Andre JC, Barkai D, Berthou JY, Boku T, Braunschweig B, Cappello F. The international exascale software project roadmap. The international journal of high performance computing applications. 2011 Feb;25(1):3-60.
- Gibbons J, Wu N. Folding domain-specific languages: deep and shallow embeddings (functional pearl). InProceedings of the 19th ACM SIGPLAN international conference on Functional programming 2014 Aug 19 (pp. 339-347).
- Sujeeth AK, Brown KJ, Lee H, Rompf T, Chafi H, Odersky M, Olukotun K. Delite: A compiler architecture for performance-oriented embedded domain-specific languages. ACM Transactions on Embedded Computing Systems (TECS). 2014 Apr 1;13(4s):1-25.
- Hennessy JL, Patterson DA. Computer architecture: a quantitative approach. Elsevier; 2011 Oct 7.
- Mernik M, Heering J, Sloane AM. When and how to develop domain-specific languages. ACM computing surveys (CSUR). 2005 Dec 1;37(4):316-44.
- DeVito Z, Hegarty J, Aiken A, Hanrahan P, Vitek J. Terra: a multi-stage language for high-performance computing. InProceedings of the 34th ACM SIGPLAN conference on Programming language design and implementation 2013 Jun 16 (pp. 105-116).
- Ragan-Kelley J, Adams A, Sharlet D, Barnes C, Paris S, Levoy M, Amarasinghe S, Durand F. Halide: Decoupling algorithms from schedules for high-performance image processing. Communications of the ACM. 2017 Dec 27;61(1):106-15.
- Oancea CE, Rauchwerger L. Logical inference techniques for loop parallelization. InProceedings of the 33rd ACM SIGPLAN conference on Programming Language Design and Implementation 2012 Jun 11 (pp. 509-520).
- Choi JW, Bedard D, Fowler R, Vuduc R. A roofline model of energy. In2013 IEEE 27th International Symposium on Parallel and Distributed Processing 2013 May 20 (pp. 661-672). IEEE.
- Puschel M, Moura JM, Johnson JR, Padua D, Veloso MM, Singer BW, Xiong J, Franchetti F, Gacic A, Voronenko Y, Chen K. SPIRAL: Code generation for DSP transforms. Proceedings of the IEEE. 2005 Feb 28;93(2):232-75.
- Logg A, Mardal KA, Wells G, editors. Automated solution of differential equations by the finite element method: The FEniCS book. Springer Science & Business Media; 2012 Feb 24.
- Silvano C, Agosta G, Bartolini A, Beccari AR, Benini L, Besnard L, Bispo J, Cmar R, Cardoso JM, Cavazzoni C, Cesarini D. Adaptive Optimization and Enforcement of Extra-Functional Properties in High Performance Computing: The ANTAREX Project. InPDP 2019-27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing 2019 Feb 13 (pp. 116-123). IEEE.
Recent Trends in Parallel Computing
| Volume | 13 |
| 01 | |
| Received | 28/01/2026 |
| Accepted | 01/02/2026 |
| Published | 20/03/2026 |
| Publication Time | 51 Days |
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