a cache-directory to reduce snoop traffic.To provide shared-memory MPs beyond these limits requires some memory to be “closer to” some processors than to others. P1 P2 Pn PN $ $ $ $ Interconnect M M Non-Uniform Memory Access - NUMA CPU/Memory busses cannot support more than ~4-8 CPUs before bus bandwidth is exceeded (the SMP “sweet spot”). Several processors share one address space conceptually a shared memory Communication is implicit read and write accesses to shared memory locations Synchronization via shared memory locations spin waiting for non-zero Atomic instructions (Test&set, compare&swap, load linked/store conditional) barriers Symmetric Multiprocessors P P P Network M Conceptual Model
#HOW MUCH IS PARALLELS MULTIPLE USERS CODE#
OS services and/or libraries used for creating tasks (processes/threads) and coordination (semaphores/barriers/locks.)įork N processes each process has a number, p, and computes istart, iend, jstart, jend for(s=0 sCooperating pieces must all execute on the same system (computer).Communication is therefore at memory access speed (very fast), and is implicit.Programs/threads communicate/cooperate via loads/stores to memory locations they share.Canonical syntax: send(process : process_id, message : string) receive(process : process_id, var message : string).Extensible to communication in distributed systems.:end foo process bar begin : y := x :end bar Potentially insecure globalint x process foo begin : x :=.No automated compiler/language exists to automate this “parallelization” process.Type of parallel architecture being used.Coordinating work and communications of those processors.Distributing the parts as tasks which are worked on by multiple processors simultaneously.The increased cache area due to multiple processors.The need to redesign algorithms to be more parallel.The portion of code that remains sequential.The need to synchronize work done on different processors.Performance of parallel algorithms is NOT limited by which factor Many applications can be (re)designed/coded/compiled to generate cooperating, parallel instruction streams – specifically to enable improved responsiveness/throughput with multiple processors.Speedup infinity, Speedup is limited to 1/seq.Sometimes, a multi-threaded design is good on uni & multi-processors e.g., throughput for a web server (that uses system multi-threading).The less communication per unit computation the better the scaling properties of the algorithm. Key property is how much communication per unit of computation.Sequential programs get no benefit from multiple processors, they must be parallelized.Total cost of ownership favors fewer systems with multiple processors rather than more systems w/fewer processors Peak performance increases linearly with more processors Adding processor/memory much cheaper than a second complete system 2P+M 2P+2M Price P+M Performance.Web browser (overlaps image retrieval with display).Most systems run multiple applications simultaneously.No matter how effective ILP/Moore’s Law, more is better.Parallel Computing Basics of Parallel Computers Shared Memory SMP / NUMA Architectures Message Passing Clusters
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