Data parallel Haskell: a status report
Proceedings of the 2007 workshop on Declarative aspects of multicore programming
Qilin: exploiting parallelism on heterogeneous multiprocessors with adaptive mapping
Proceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture
Declarative data-parallel programming with the accelerator system
Proceedings of the 5th ACM SIGPLAN workshop on Declarative aspects of multicore programming
Nikola: embedding compiled GPU functions in Haskell
Proceedings of the third ACM Haskell symposium on Haskell
Accelerating Haskell array codes with multicore GPUs
Proceedings of the sixth workshop on Declarative aspects of multicore programming
Copperhead: compiling an embedded data parallel language
Proceedings of the 16th ACM symposium on Principles and practice of parallel programming
HPCC '11 Proceedings of the 2011 IEEE International Conference on High Performance Computing and Communications
Compiling a high-level language for GPUs: (via language support for architectures and compilers)
Proceedings of the 33rd ACM SIGPLAN conference on Programming Language Design and Implementation
River trail: a path to parallelism in JavaScript
Proceedings of the 2013 ACM SIGPLAN international conference on Object oriented programming systems languages & applications
| Hi-index | 0.00 |
Today's Internet is long past static web pages filled with HTML-formatted text sprinkled with an occasional image or animation. We have entered an era of Rich Internet Applications executed locally on Internet clients such as web browsers: games, physics engines, image rendering, photo editing, etc. Yet today's languages used to program Internet clients have limited ability to tap to the computational capabilities of the underlying, often heterogeneous, platforms. In this paper we present how a Domain Specific Language(DSL) can be integrated into ActionScript, one of the most popular scripting languages used to program Internet clients and a close cousin of JavaScript. We demonstrate how our DSL, called ASDP (ActionScript Data Parallel), can be used to enable data parallelism for existing sequential programs. We also present a prototype of a system where data parallel workloads can be executed on either CPU or a GPU, with the runtime system transparently selecting the best processing unit, depending on the type of workload as well as the architecture and current load of the execution platform. We evaluate performance of our system on a variety of benchmarks, representing different types of workloads: physics, image processing, scientific computing and cryptography.