We enhance the well-established software combining synchronization technique to create combining funnels. Previous software combining methods used a statically assigned tree whose depth was logarithmic in the total number of processors in the system. On shared memory multiprocessors the new method allows one to dynamically build combining trees with depth logarithmic in the actual number of processors concurrently accessing the data structure. The structure is comprised from a series of combining layers through which processors' requests are funneled. These layers use randomization instead of a rigid tree structure to allow processors to find partners for combining. By using an adaptive scheme the funnel can change width and depth to accommodate different access frequencies without requiring global agreement as to its size. Rather, processors choose parameters of the protocol privately, making this scheme very simple to implement and tune. When we add an elimination'' mechanism to the funnel structure, the randomly constructed tree'' is transformed into a ``forest'' of disjoint (and on average shallower) trees of requests, thus enhancing the level of parallelism and decreasing latency. We present two new linearizable combining funnel based data structures: a fetch-and-add object and a stack. We study the performance of these structures by benchmarking them against the most efficient