By R. Aversa, N. Mazzocca, U. Villano (auth.), Victor Malyshkin (eds.)
This e-book constitutes the refereed court cases of the fifth foreign Congress on Parallel Computing applied sciences, PaCT-99, held in St. Petersburg, Russia in September 1999.
The forty seven revised papers offered have been conscientiously reviewed and chosen from greater than a hundred submissions. The papers handle all present matters in parallel processing starting from concept, algorithms, programming, and software program to implementation, architectures, undefined, and purposes.
Read Online or Download Parallel Computing Technologies: 5th International Conference, PaCT-99 St. Petersburg, Russia, September 6–10, 1999 Proceedings PDF
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Extra info for Parallel Computing Technologies: 5th International Conference, PaCT-99 St. Petersburg, Russia, September 6–10, 1999 Proceedings
Sample text
Then, each of them was traced and simulated cycle-by-cycle for 100 million instructions. 32 Chi-Hung Chi and Jun-Li Yuan Table 2. Simulation Parameters for the Baseline Architecture Processor Specification: UltraSPARC ISA compatiable Superscalar architecture with 3 integer units, 3 floating pt. units, 1 branch unit, 1 LOAD/STORE unit Out-of-order execution, register renaming, 2-bit branch predictor, reorder-buffer with 64 entries. 1st Level Data Cache 1st Level Instruction Cache: Cache Size: 32 Kbytes Cache Size: 32 Kbytes Block Size: 32 bytes Block Size: 32 bytes Associativity: direct-mapped Associativity: direct-mapped Replacement: LRU Replacement: LRU Placement: write-back Placement: write-back Mem.
This simple idea can be expressed by the following FAN algorithm: pol-eval1 (in ys : arr m], as : arr n], out zs : arr m]) ts = scanL ( ) (copy n ys) ds = map ( sa ) (as,ts) zs = reduce (+) ds The algorithm takes as input vectors as and ys (represented as arrays) and proceeds as follows: { copy n ys returns an array of n copies of vector ys, { scanL ( ) computes ts : arr n] m], where ts i] = ysi for all i, { map +sa (as,ts) computes ds : arr n] m], such that ds i] = ai y1i : : : ai ymi ], { reduce (+) ds sumsPup all rows of ds P elementwise and puts the results in zs, n n such that: zs = ( i=1 ai y1i ) : : : ( i=1 ai ymi )].
E. i = j), the cache block to be prefetched will be adjusted to either i+1 or i-1, depending on whether i < j or i > j. From Table 1, almost 40% of the cache misses occur under this situation, either as complete misses or partial misses. This gives an estimate of its significance. The sequential unification and aggressive lookahead mechanisms are orthogonal to each other. Hence, they can work collaboratively to improve cache performance further. In this case, the sequential unification will work on the aggressive lookahead prefetch request whenever it occurs.