Why Linda?

Portability
Linda is available on a large number of parallel computer systems, including shared-memory computers, distributed- memory computers, and networks. Most Linda programs written for one machine run without change on others, facilitating single-source portable parallel codes for all architectures.

Ease of Use
Linda implements parallelism via a global, logically-shared memory, using a small number of simple but powerful operations. The entire approach is easy to understand and quickly mastered.

Investment Preservation
Linda enables you to transform existing sequential C, C++, or Fortran programs into parallel codes quickly and easily. Your return on those software investments is not only preserved with Linda, but multiplied many times over.

Affordability
While costly and specialized parallel computers once were required, today you can start out with an existing departmental workstation network, and combine cycles together to achieve parallel performance gains, increased job throughput, and improved resource utilization levels.

Support for Heterogeneity
Linda makes operation on heterogeneous clusters easy and almost automatic by providing transparent data conversion between architectures and well-thought-out facilities for node selection, directory mapping, and program startup.

Scalability
Linda's exceptionally low overhead results in high performance low-level speed from a high-level tool.

Effective Code Development
Since Linda is a language extension implemented via pre-compile and pre-link processing, it is possible to optimize code automatically and provide extensive error reporting during the compile and link phases. Debugging is simple and effective. Linda systems include Tuplescope, a graphical debugging tool that makes it easy to debug process management and interprocess communication. Moreover, both Linda and Tuplescope are fully compatible with standard UNIX debuggers such as DBX and GDB.

High Performance on Special Parallel Architectures
Linda takes full advantage of the underlying architectures of all shared-memory machines and distributed-memory parallel machines from vendors such as IBM, Cray, SGI, HP, and Hitachi. Custom run-time systems make it possible to target a single set of sources to any of these.

Dynamic Load Balancing
The virtual shared memory data model makes it easy to build software using techniques that lead to automatic load balancing - even among heterogeneous processors.

 
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