This paper presents novel sampling-based techniques for collecting statistical profiles of register contents, data values, and other information associated with instructions, such as memory latencies. Values of interest are sampled in response to periodic interrupts. The resulting value profiles can be analyzed by programmers and optimizers to improve the performance of production uniprocessor and multiprocessor systems.Our value sampling system extends the DCPI continuous profiling infrastructure, and inherits many of its desirable properties: our value profiler has low overhead (approximately 10% slowdown); it profiles all the code in the system, including the operating system kernel; and it operates transparently, without requiring any modifications to the profiled code.

Reconfigurable computing devices have achieved substantial performance improvements over conventional processors on some computational kernels. These benefits derive from hardware customization which avoids the mismatch between the basic requirements of the algorithms and the architectures of the processors. A reconfigurable fabric alone is not sufficient for general-purpose computing since it can be ill-suited to executing entire programs due to space limitations, dataflow-centricity, and inefficiency at implementing some operations (e.g. floating-point arithmetic). These observations have led to the appearance of numerous designs which place some form of reconfigurable logic under the control of a general-purpose processor. The authors explore the ways in which a reconfigurable fabric can be interfaced with a general-purpose processor. While off-chip reconfigurable fabrics have proven to be quite effective at performing streaming, data-intensive computations, they require large streams of data to overcome the latency between the devices. We explore the design space for an on-chip fabric, i.e., a reconfigurable function unit (RFU). An RFU allows smaller portions of application to be mapped to the fabric in the form of custom instructions. Though the speedups achieved for stream based computations will in general be much larger than those for custom instructions, they are limited to a smaller class of applications. Custom instructions, however, can be found in a larger class of programs, and compiler techniques can automatically create them.