A recent development in radio astronomy is to replace traditional dishes with many small antennas. The signals are combined to form one large, virtual telescope. The enormous data streams are cross-correlated to filter out noise. This is especially challenging, since the computational demands grow quadratically with the number of data streams. Moreover, the correlator is not only computationally intensive, but also very I O intensive. The LOFAR telescope, for instance, will produce over 100 terabytes per day. The future SKA telescope will even require in the order of exaflops, and petabits s of I O. A recent trend is to correlate in software instead of dedicated hardware, to increase flexibility and to reduce development efforts. We evaluate the correlator algorithm on multi-core CPUs and many-core architectures, such as NVIDIA and ATI GPUs, and the Cell B.E. The correlator is a streaming, real-time application, and is much more I O intensive than applications that are typically implemented on many-core hardware today. We compare with the LOFAR production correlator on an IBM Blue Gene P supercomputer. We investigate performance, power efficiency, and programmability. We identify several important architectural problems which cause architectures to perform suboptimally. Our findings are applicable to data-intensive applications in general. The processing power and memory bandwidth of current GPUs are highly imbalanced for correlation purposes. While the production correlator on the Blue Gene P achieves a superb 96 of the theoretical peak performance, this is only 16 on ATI GPUs, and 32 on NVIDIA GPUs. The Cell B.E. processor, in contrast, achieves an excellent 92 . We found that the Cell B.E. and NVIDIA GPUs are the most energy-efficient solutions, they run the correlator at least 4 times more energy efficiently than the Blue Gene P. The research presented is an important pathfinder for next-generation telescopes.
A software-managed cache is used on the Cell B.E. processor. GPUs typically have a small amount of shared memory that can be used in a similar way @cite_8 . An important difference is that in the Cell B.E. the memory is private for a thread, while with GPUs all threads on a multiprocessor share the memory. The available memory per thread is also much smaller. We applied the technique described in @cite_8 , but found it did not increase performance for our application.
@cite_8
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We present a technique for designing memory-bound algorithms with high data reuse on Graphics Processing Units (GPUs) equipped with close-to-ALU software-managed memory. The approach is based on the efficient use of this memory through the implementation of a software-managed cache. We also present an analytical model for performance analysis of such algorithms. We apply this technique to the implementation of the GPU-based solver of the sum-product or marginalize a product of functions (MPF) problem, which arises in a wide variety of real-life applications in artificial intelligence, statistics, image processing, and digital communications. Our motivation to accelerate MPF originated in the context of the analysis of genetic diseases, which in some cases requires years to complete on modern CPUs. Computing MPF is similar to computing the chain matrix product of multi-dimensional matrices, but is more difficult due to a complex data-dependent access pattern, high data reuse, and a low compute-to-memory access ratio. Our GPU-based MPF solver achieves up to 2700-fold speedup on random data and 270-fold on real-life genetic analysis datasets on GeForce 8800GTX GPU from NVIDIA over the optimized CPU version on an Intel 2.4GHz Core 2 with a 4MB L2 cache.