To process a large volume of data, modern data management systems use a collection of machines connected through a network. This paper looks into the feasibility of scaling up such a shared-nothing system while processing a compute- and communication-intensive workload---processing distributed joins. By exploiting multiple processing cores within the individual machines, we implement a system to process database joins that parallelizes computation within each node, pipelines the computation with communication, parallelizes the communication by allowing multiple simultaneous data transfers (send/receive), and removes synchronization barriers (a scalability bottleneck in a distributed data processing system). Our experimental results show that using only four threads per node the framework achieves a 3.5x gains in intra-node performance while compared with a single-threaded counterpart. Moreover, with the join processing workload the cluster-wide performance (and speedup) is observed to be dictated by the intra-node computational loads; this property brings a near-linear speedup with increasing nodes in the system, a feature much desired in modern large-scale data processing system.