text: This work proposes an Application-Specific System Processor (ASSP) hardware for the Secure Hash Algorithm 1 (SHA-1) algorithm. The proposed hardware was implemented in a Field Programmable Gate Array (FPGA) Xilinx Virtex 6 xc6vlx240t-1ff1156. The throughput and the occupied area were analyzed for several implementations in parallel instances of the hash algorithm. The results showed that the hardware proposed for the SHA-1 achieved a throughput of 0.644 Gbps for a single instance and slightly more than 28 Gbps for 48 instances in a single FPGA. Various applications such as password recovery, password validation, and high volume data integrity checking can be performed efficiently and quickly with an ASSP for SHA1.
  @cite_5: High performance computing is required in a number of data-intensive domains. CPU and GPU clusters are one of the most progressive branches in a field of parallel computing and data processing nowadays. Cloud computing has recently emerged as one of the buzzwords in the ICT industry. It offers suitable abstractions to manage the complexity of large data processing and analysis in various domains. This paper addresses issues associated with distributed computational system and the application of mixed GPU&CPU technology to data intensive computation. We describe a hybrid cluster formed by devices from different vendors (Intel, AMD, NVIDIA). Two variants of software environment that hides the heterogeneity of our hardware platform and provides tools for solving complex scientific and engineering problems are presented and discussed. The first solution (HGCC) is a software platform for data processing in heterogenous CPU GPU clusters. The second solution (HGCVC) is an extension version of the previous one. The cloud technology is incorporated to the HGCC framework. The results of numerical experiments performed for parallel implementations of password recovery algorithms are presented to illustrate the performance of our systems.
  @cite_6: Today Graphics Processing Units (GPUs) are a largely underexploited resource on existing desktops and a possible cost-effective enhancement to high-performance systems. To date, most applications that exploit GPUs are specialized scientific applications. Little attention has been paid to harnessing these highly-parallel devices to support more generic functionality at the operating system or middleware level. This study starts from the hypothesis that generic middleware-level techniques that improve distributed system reliability or performance (such as content addressing, erasure coding, or data similarity detection) can be significantly accelerated using GPU support. We take a first step towards validating this hypothesis and we design StoreGPU, a library that accelerates a number of hashing-based middleware primitives popular in distributed storage system implementations. Our evaluation shows that StoreGPU enables up twenty five fold performance gains on synthetic benchmarks as well as on a high-level application: the online similarity detection between large data files.
summary: Works with SHA-1 implementation on other hardware platforms can be found in @cite_5 and @cite_6 in which comparisons between Graphics Processing Units (GPUs) and CPUs were performed. The GPUs NVIDIA Tesla M2050 with @math CUDA cores and AMD FirePro V7800 with @math stream processors could achieve throughput peaks of up to @math Gbps.