As problem size increases dramatically and quickly, many scientific computing applications, such as molecular dynamics and many-particle collision simulations, exhibit high demand on data capacity and computability of the hosting computer systems. Scalable solutions are on demand. Neither well optimized sequential algorithms nor expensive shared memory parallel computers could be the ultimate choice for such large-scale problems. This paper intends to solve this scalability problem by taking advantage of the availability of commodity computers. Aggregated memory and networked computers satisfy the capacity and computability requests. To overcome the strong event dependency nature in particle collision simulations, a speculative execution scheme is proposed to exploit parallelism. Performance analysis and experiments are provided to show the balance between scalability and performance gains.