This paper proposes an effective branch folding technique which combines branch instructions with predicted instructions. This technique can be implemented using an instruction queue, which buffers prefetched instructions. Most of the instructions in the instruction queue are forwarded to the execution unit in sequence. Branch instructions, however, are combined with predicted instructions in the instruction queue and these folded instructions are forwarded to the execution unit. Miss-prediction can be recovered by flushing folded instructions without processor state recovery and by restarting from the other path. Simulation and implementation results show that both performance and power consumption are significantly improved with little additional hardware cost.

In an adaptive load balancing, the location policy to determine a destination node for transferring tasks can be classified into three categories: dynamic selection, random selection, and state polling. The dynamic selection immediately determines a destination node by exploiting the state information broadcasted from other nodes. It not only requires the overheads of collecting the state information, but may cause an unpredictable behavior unless the state information is accurate. Also, it may not guarantee even load distribution. The random selection determines a destination node at random. The state polling determines a destination node by polling other nodes. It may cause some problems such as useless polling, unachievable load balancing, and system instability. A new Sender-initiated Adaptive LOad balancing scheme (SALO) is presented to remedy the above problems. It determines a destination node by exploiting the predictable state knowledge and by polling the destination node. It can determine a good destination with minimal useless polling and guarantee even load distribution. Also, it has an efficient mechanism and good data structure to collect the state information simply. An analytic model is developed to compare with other well known schemes. The validity of the model is checked with an event-driven simulation. With the model and the simulation result, it is shown that SALO yields a significant improvement over other schemes, especially at high system loads.

In this paper, we consider linearization of nonlinear datalog programs with multiple bilinear rules and multiple linear rules. If a nonlinear program can be linearized, it is possible to process queries on the program efficiently by using well-known cost-effective techniques for linear programs. We define a transformation, called Right-Linear-First (RLF) transformation, for linearizing such nonlinear programs. A nonlinear program is RLF-linearizable if it is logically equivalent to its RLF-transformed program. We present three sufficient conditions, called LCR-consistency, LCRN1-consistency, and LCRN2-consistency, for identifying such RLF-linearizable programs. These conditions can be tested in polynomial time. Our work presented in this paper extends the work on ZYT-linearizability in a sense that RLF-linearizability considers multiple bilinear rules with multiple linear rules.