Traditional supercomputers have focused on dense computation performance as exemplified by HPL. Graph processing applications differ with extreme irregularity ($10^{9}$imbalance in skewed, real-world graphs) that produces unpredictable work, parallelism, memory access, and communication. Together, these make scalable performance and programming difficult. We describe the UpDown system architecture, co-designed for irregular graph computations. UpDown provides efficient fine-grained thread invocations ($\sim$10 instructions), direct messaging (no network interface card) for scalable local and global messaging, and split-transaction memory operations that enable extremely high memory bandwidth. Combined with architectural support for global addressing and an aggressive network design, these UpDown features enable direct exploitation of edge and vertex parallelism, using it to deliver breakthrough graph processing performance and programmability. We evaluate the performance of the UpDown system using a challenging suite of graph applications (Pagerank, Breadth-first Search, Triangle Counting, Partial Match, etc). For a single-node, results show 100-fold performance advantage over multicore CPUs. Compared to today's fastest scalable parallel computers UpDown achieves 1000-fold performance increases. UpDown delivers these levels of performance with high-level programmability, these programs directly express vertex-edge parallelism which UpDown exploits directly in hardware.