We investigate scheduling algorithms for distributed transactional memory systems where transactions residing at nodes of a communication graph operate on shared, mobile objects. A transaction requests the objects it needs, executes once those objects have been assembled, and then possibly forwards those objects to other waiting transactions. Minimizing execution time in this model is known to be NP-hard for arbitrary communication graphs, and also hard to approximate within any factor smaller than the size of the graph. Nevertheless, networks on chips, multi-core systems, and clusters are not arbitrary. Here, we explore efficient execution schedules in specialized graphs likely to arise in practice: Clique, Line, Grid, Cluster, Hypercube, Butterfly, and Star. In most cases, when individual transactions request k objects, we obtain solutions close to a factor O(k) from optimal, yielding near-optimal solutions for constant k. These execution times approximate the TSP tour lengths of the objects in the graph. We show that for general networks, even for two objects (k = 2), it is impossible to obtain execution time close to the objects’ optimal TSP tour lengths, which is why it is useful to consider more realistic network models. To our knowledge, this is the first attempt to obtain provably fast schedules for distributed transactional memory.

我们研究了分布式事务存储系统的调度算法，其中驻留在通信图节点上的事务在共享的移动对象上进行操作。一个事务请求它需要的对象，一旦这些对象被组装就执行，然后可能将那些对象转发给其他等待的事务。对于任何通信图来说，在该模型中最小化执行时间都是NP难的，并且在小于图的大小的任何因数内也难以近似。但是，片上网络，多核系统和群集并不是任意的。在这里，我们在实践中可能会出现的专用图中探索有效的执行计划：Clique，Line，Grid，Cluster，Hypercube，Butterfly和Star。在大多数情况下，当单个交易请求k对象，我们从最优解中获得接近因数O（k）的解，得出常数k的接近最优解。这些执行时间近似于图中对象的TSP巡视长度。我们表明，对于一般网络，即使对于两个对象（k = 2），也无法获得接近对象最佳TSP游程长度的执行时间，这就是为什么考虑更现实的网络模型很有用的原因。据我们所知，这是为分布式事务性存储器获得可证明的快速调度的首次尝试。